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NI Vision for Visual Basic User Manual

1. cwimaqMeasurementMaxFeretDiameterOrientation The angle of the line segment connecting the two perimeter points that are the furthest apart cwimaqMeasurementMaxFeretDiameterStartX X coordinate of the start of the line segment connecting the two perimeter points that are the furthest apart cwimaqMeasurementMaxFeretDiameterStartY Y coordinate of the start of the line segment connecting the two perimeter points that are the furthest apart cwimaqMeasurementMaxHorizSegmentLengthLeft X coordinate of the leftmost pixel in the longest row of contiguous pixels in the particle cwimaqMeasurementMaxHorizSegmentLengthRight X coordinate of the rightmost pixel in the longest row of contiguous pixels in the particle cwimaqMeasurementMaxHorizSegmentLengthRow Y coordinate of all of the pixels in the longest row of contiguous pixels in the particle NI Vision for Visual Basic User Manual 4 8 ni com Chapter 4 Performing Particle Analysis Table 4 1 Measurement Types Continued Measurement Description cwimaqMeasurementMomentOflnertiaXX The moment of inertia in the X direction twice cwimaqMeasurementMomentOflInertiaX XX The moment of inertia in the X direction three times cwimaqMeasurementMomentOflInertiaX XY The moment of inertia in the X direction twice and the Y direction once cwimaqMeasurementMomentOflnertiaX Y The moment of inertia in the X and Y
2. Color Image Red Green ad or Blue Hue Saturation Intensity Hue Saturation or Luminance Hue Saturation Value J J oJ J J oJ oJ 2 8 bit Image Processing Red Green Blue Hue Saturation Intensity Hue Saturation or Color Image ad or Luminance Hue Saturation Value or Figure 3 3 Primary Components of an 32 Bit Color Image Color Image O R m Red Green Blue 16 bit Image Processing Red Color Green 64 Image Blue Figure 3 4 Primary Components of a 64 Bit Color Image A color pixel encoded as a Long value can be decomposed into its individual components using CWIMAQVision IntegerToColorValue You can convert a pixel value represented in any color model into its components in any other color model using CWIMAQVision ColorValueConversion2 NI Vision for Visual Basic User Manual 3 8 ni com Comparing Colors Chapter 3 Making Grayscale and Color Measurements You can use the color matching capability of NI Vision to compare or evaluate the color content of an image or regions in an image Complete the following steps to compare colors using color matching 1 Select an image containing
3. Specify the granularity required to represent the color information Choose colors that you want to ignore during matching 3 9 NI Vision for Visual Basic User Manual Chapter 3 Making Grayscale and Color Measurements Specifying the Color Information to Learn Because color matching only uses color information to measure similarity the image or regions in the image representing the object should contain only the significant colors that represent the object as shown in Figure 3 5a Figure 3 5b illustrates an unacceptable region containing background colors Figure 3 5 Template Color Information The following sections specify when to learn the color information associated with an entire image a region in an image or multiple regions in an image Using the Entire Image You can use an entire image to learn the color spectrum that represents the entire color distribution of the image In a fabric identification application for example an entire image can specify the color information associated with a certain fabric type as shown in Figure 3 6 Figure 3 6 Using the Entire Image to Learn Color Distribution NI Vision for Visual Basic User Manual 3 10 ni com Chapter 3 Making Grayscale and Color Measurements Using a Region in the Image You can select a region in the image to provide the color information for comparison A region is helpful for pulling out the useful color information in
4. Compare to Golden Template Use CWIMAQVision CompareGoldenTemplate to inspect your image based on differences in intensity Use the NI Vision Template Editor to learn golden templates for inspection Go to Start All Programs National Instruments Vision Template Editor to launch the NI Vision Template Editor Use the methods described in Chapter 4 Performing Particle Analysis to analyze the resulting binary image National Instruments Corporation 5 35 NI Vision for Visual Basic User Manual Chapter 5 Performing Machine Vision Tasks Verify Characters Use character verification functions to verify characters in your image Before you verify text you must train the OCR Session with samples of the characters using the NI OCR Training Interface Go to Start All Programs National Instruments Vision OCR Training to launch the NI OCR Training Interface You must then designate a reference character for each of the character classes in your character set The characters you want to verify are compared against these reference characters and are assigned a score based on this comparison After you have trained the samples of the characters and assigned reference characters use the following functions to verify the characters e Use NIOCR ReadOCRFile to read in a character set file that you created using the OCR Training Interface e UseNIOCR VerifyText to verify the characters inside the ROI of the image under inspection Di
5. e Proper closing Removes dark pixels isolated in bright regions and smooths the inner contours of particles e Auto median Generates simpler particles that have fewer details FFT Use the Fast Fourier Transform FFT to convert an image into its frequency domain In an image details and sharp edges are associated with mid to high spatial frequencies because they introduce significant gray level variations over short distances Gradually varying patterns are associated with low spatial frequencies An image can have extraneous noise such as periodic stripes introduced during the digitization process In the frequency domain the periodic pattern is reduced to a limited set of high spatial frequencies Also the imaging setup may produce non uniform lighting of the field of view which produces an image with a light drift superimposed on the information you want to analyze In the frequency domain the light drift appears as a limited set of low frequencies around the average intensity of the image which is the DC component You can use algorithms working in the frequency domain to isolate and remove these unwanted frequencies from the image Complete the following steps to obtain an image in which the unwanted pattern has disappeared but the overall features remain 1 Use CWIMAQVision FFT to convert an image from the spatial domain to the frequency domain This method computes the FFT of the image and results in a complex image r
6. nine of the most commonly used measurements including the particle area bounding rectangle and center of mass The bounding rectangle is returned as one measurement but contains four measurement elements The center of mass is returned as one measurement but contains two elements e CWIMAQVision ParticleMeasurement This method takes the measurement you want to apply to all particles and returns an array that contains the specified measurement for each particle Table 4 1 lists all of the measurements that CWIMAQVision ParticleMeasurement returns Table 4 1 Measurement Types Measurement Description cwimaqMeasurementArea Area of the particle cwimaqMeasurementAreaByImageArea Percentage of the particle Area covering the Image Area cwimaqMeasurementAreaB yParticleAndHolesArea Percentage of the particle Area in relation to its Particle amp Holes Area cwimaqMeasurementAverageHorizSegmentLength Average length of a horizontal segment in the particle cwimaqMeasurementAverage VertSegmentLength Average length of a vertical segment in the particle cwimaqMeasurementBoundingRectBottom Y coordinate of the lowest particle point cwimaqMeasurementBoundingRectDiagonal Distance between opposite corners of the bounding rectangle cwimaqMeasurementBoundingRectHeight Distance between the Y coordinate of highest particle point and the Y coordinate of the lowest particle point Nationa
7. CWIMAQVision CreateCWIMAQImage method Creating NI Vision Applications Figures 1 1 and 1 2 illustrate the steps for creating an application with NI Vision Figure 1 1 describes the general steps for designing an NI Vision application The last step in Figure 1 1 is expanded upon in Figure 1 2 You can use a combination of the items in the last step to create a NI Vision application For more information about items in either diagram refer to the corresponding chapter listed beside the item 3 Note Diagram items enclosed with dashed lines are optional steps NI Vision for Visual Basic User Manual 1 4 ni com Chapter 1 Introduction to NI Vision Set Up Your Imaging System Chapter 6 Calibrating Images Calibrate Your Imaging System Create an Image Acquire or Read an Image Chapter 2 Getting Measurement Ready Images Display an Image Attach Calibration Information Analyze an Image Improve an Image Make Measurements or Identify Objects in an Image Using O Grayscale or Color Measurements and or Particle Analysis and or Machine Vision Figure 1 1 General Steps for Designing a Vision Application National Instruments Corporation 1 5 NI Vision for Visual Basic User Manual Chapter 1 Introduction to NI Vision Chapter 3 Making Grayscale and Color Define Regions of Interest Create a Binary Image Chapter 4 Performin
8. CWIMAQVision WriteMultipleGeometricTemplateFile to save the multiple geometric templates created by this step to a file Use CWIMAQVision ReadMultipleGeometricTemplateFile to read the multiple geometric template file within your application during the matching process Use the methods described in the Setting Matching Parameters and Tolerances section to define how each template matches the target image Use CWIMAQMu1tipleGeometricTemplate SetMatchOptions to set the options for the corresponding template 3 Note Ifyou save the multiple geometric template to file after you set the options these options will be loaded within your application when you load the multiple geometric template file Test the multiple template search algorithm on test images using CWIMAQVision MatchMultipleGeometricPatterns to match all of the templates The importance of this step is for the same reason explained in the Testing the Search Algorithm on Test Images section Finding Points Using Color Pattern Matching Color pattern matching algorithms provide a quick way to locate objects when color is present Use color pattern matching under the following circumstances The object you want to locate has color information that is very different from the background and you want to find a very precise location of the object in the image The object to locate has grayscale properties that are very difficult to characterize or that are very similar to other object
9. When you have an automated application you need to define regions of interest programmatically You can programmatically define regions by providing basic parameters that describe the region you want to define You can specify a rotated rectangle by creating a CWIMAQRotatedRectangle object and setting the coordinates of the center width height and rotation angle You can specify an annulus by creating a CWIMAQAnnulus object and setting the coordinates of the center inner radius outer radius start angle and end angle You can specify a point by setting its x coordinates and y coordinates You can specify a line by setting the coordinates of the start and end points Refer to Chapter 3 Making Grayscale and Color Measurements for more information about defining regions of interest Find Measurement Points After you set regions of inspection locate points in the regions on which you can base measurements You can locate measurement points using edge detection pattern matching color pattern matching and color location Finding Features Using Edge Detection Use the edge detection tools to identify and locate sharp discontinuities in an image Discontinuities typically represent abrupt changes in pixel intensity values which characterize the boundaries of objects Finding Lines or Circles If you want to find points along the edge of an object and find a line describing the edge use CWMachineVision FindStraightEdge and CWMac
10. directions cwimaqMeasurementMomentOflnertiaX Y Y The moment of inertia in the X direction once and the Y direction twice cwimaqMeasurementMomentOfInertia Y Y The moment of inertia in the Y direction twice cwimaqMeasurementMomentOfInertiaY Y Y The moment of inertia in the Y direction three times cwimaqMeasurementNormMomentOflnertiaxX The normalized moment of inertia in the X direction twice cwimaqMeasurementNormMomentOflnertiaxX XX The normalized moment of inertia in the X direction three times cwimaqMeasurementNormMomentOflnertiaxX XY The normalized moment of inertia in the X direction twice and the Y direction once cwimaqMeasurementNormMomentOflnertiaX Y The normalized moment of inertia in the X and Y directions cwimaqMeasurementNormMomentOflnertiaX Y Y The normalized moment of inertia in the X direction once and the Y direction twice cwimaqMeasurementNormMomentOfInertiaY Y The normalized moment of inertia in the Y direction twice cwimaqMeasurementNormMomentOflnertiaY Y Y The normalized moment of inertia in the Y direction three times cwimaqMeasurementNumberOfHoles Number of holes in the particle National Instruments Corporation 4 9 NI Vision for Visual Basic User Manual Chapter 4 Performing Particle Analysis Table 4 1 Measurement Types Continued Measurement Description cwimaqMeasurementNumberOfHorizSe
11. Amp Fuses Figure 5 15 Selecting a Search Area for Color Pattern Matching National Instruments Corporation 5 27 NI Vision for Visual Basic User Manual Chapter 5 Performing Machine Vision Tasks The time required to locate a pattern in an image depends on both the template size and the search area By reducing the search area or increasing the template size you can reduce the required search time Increasing the size of the template can improve search time but doing so reduces match accuracy if the larger template includes an excess of background information Setting Matching Parameters and Tolerances Every color pattern matching algorithm makes assumptions about the images and color pattern matching parameters used in machine vision applications These assumptions work for a high percentage of the applications In some applications the assumptions used in the algorithm are not optimal In such cases you must modify the color pattern matching parameters To efficiently select the best pattern matching parameters for the application you must have a clear understanding of the application and the images you want to process The following sections discuss parameters of the NI Vision color pattern matching algorithm and how they influence the algorithm Color Sensitivity Use the color sensitivity to control the granularity of the color information in the template image If the background and objects in the image contain color
12. FindEdges3 to find edge points along a contour You can find the first edge last edge or all edges along the contour Use CWIMAQVision SimpleEdge when the image contains little noise and the object and background are clearly differentiated Otherwise use CWIMAQVision FindEdges3 These methods require you to input the coordinates of the points along the search contour Use CWIMAQVision RegionsProfile to obtain the coordinates from a CWIMAQRegions object that describes the contour If you have a straight line use CWIMAQVision GetPointsOnLine to obtain the points along the line instead of using regions These methods determine the edge points based on their contrast and slope You can specify if you want to find the edge points using subpixel accuracy National Instruments Corporation 5 11 NI Vision for Visual Basic User Manual Chapter 5 Performing Machine Vision Tasks Finding Edge Points Along Multiple Search Contours Use the CWIMAQVision Rake CWIMAQVision Spoke and CWIMAQVision ConcentricRake methods to find edge points along multiple search contours These methods find only the first edge that meets the criteria along each contour Pass ina CWIMAQRegions object to define the search region for these methods CWIMAQVision Rake works on a rectangular search region The search lines are drawn parallel to the orientation of the rectangle Control the number of search lines in the region by specifying the distance in pix
13. Method to Build the Coordinate Transformation Figure 5 4 guides you through choosing the best method for building a coordinate transformation for the application Object positioning accuracy better than 65 degrees The object under inspection has a straight No distinct edge main axis The object contains a second distinct edge not parallel to the main axis in the same search area The object contains a second distinct edge not parallel to the main axis ina separate search area Yes Build a coordinate transformation based on edge detection using a single search area Build a coordinate transformation based on edge detection using two distinct search areas Object positioning accuracy better than 5 degrees y Build a coordinate transformation based on pattern matching Build a coordinate transformation based on pattern matching shift invariant strategy rotation invariant strategy Figure 5 4 National Instruments Corporation 5 7 Building a Coordinate Transformation NI Vision for Visual Basic User Manual Chapter 5 Performing Machine Vision Tasks Set Search Areas Defining Select ROIs in the images to limit the areas in which you perform the processing and inspection You can define ROIs interactively or programmatically Regions Interactively Follow these steps to interactively
14. The process of improving the quality of an image that you acquire from a sensor in terms of signal to noise ratio image contrast edge definition and so on A file containing pixel data and additional information about the image Defines how an image is stored in a file Usually composed of a header followed by the pixel data A binary image that isolates parts of a source image for further processing A pixel in the source image is processed if its corresponding mask pixel has a non zero value A source pixel whose corresponding mask pixel has a value of 0 is left unchanged The gradation of colors used to display an image on screen usually defined by a CLUT NI Vision for Visual Basic User Manual G 8 ni com image processing image source imaging inner gradient inspection inspection function instrument driver intensity intensity calibration intensity profile intensity range intensity threshold National Instruments Corporation G 9 Glossary Encompasses various processes and analysis functions that you can apply to an image The original input image Any process of acquiring and displaying images and analyzing image data Finds the inner boundary of objects The process by which parts are tested for simple defects such as missing parts or cracks on part surfaces Analyzes groups of pixels within an image and returns information about the size shape position and pixel connectivity Typical applic
15. The radius of the dots must be 6 10 pixels The center to center distance between dots must range from 18 to 32 pixels as shown in Figure 6 1 The minimum distance between the edges of the dots must be 6 pixels as shown in Figure 6 1 uf hv eee 0000o E E E EE E E EE E EE E E EE E EE E 1 Center to Center Distance 2 Center of Grid Dots 3 Distance Between Dot Edges Figure 6 1 Defining a Calibration Grid 5 Note You can use the calibration grid installed with NI Vision at Start All Programs National Instruments Vision Documentation Calibration Grid The dots have radii of 2 mm and center to center distances of 1 cm Depending on the printer these measurements may change by a fraction of a millimeter You can purchase highly accurate calibration grids from optics suppliers such as Edmund Industrial Optics NI Vision for Visual Basic User Manual 6 2 ni com Chapter 6 Calibrating Images Defining a Reference Coordinate System To express measurements in real world units you must define a coordinate system in the image of the grid Use CWIMAQLearnCalibrationOptions CalibrationAxisInfo to define a coordinate system by its origin angle and axis direction The origin expressed in pixels defines the center of the coordinate system The angle specifies the orientation of the coordinate system with respect to the angle of the topmost row of dots in the grid image The calibration procedure a
16. also need to specify the parameters for edge detection and the separation between the search lines that you want to use within the search region to find the edges These methods work directly on the image under inspection and they output the coordinates of all the edge points that they find The following list describes the available clamp methods CWMachineVision MeasureMaximumDistance Measures the largest separation between two edges in a rectangular search region e CWMachineVision MeasureMinimumDistance Measures the smallest separation between two edges in a rectangular search region Use CWIMAQVision FindPointDistances to compute the distances between consecutive pairs of points in an array of points You can obtain these points from the image using any one of the feature detection methods described in the Find Measurement Points section of this chapter Analytic Geometry Measurements Use the following CWIMAQVision methods to make geometrical measurements from the points you detect in the image e FitLine Fits a line to a set of points and computes the equation of the line e FitCircle2 Fits a circle to a set of at least three points and computes its area perimeter and radius e FitEllipse2 Fits an ellipse to a set of at least six points and computes its area perimeter and the lengths of its major and minor axis e FindIntersectionPoint Finds the intersection point of two lines specified by their start and
17. an image mask Defining Regions Interactively You can interactively define an ROI in a viewer that displays an image Use the tools from the right click menu to interactively define and manipulate the ROIs Table 3 1 describes each of the tools and the manner in which you use them National Instruments Corporation 3 1 NI Vision for Visual Basic User Manual Chapter 3 Making Grayscale and Color Measurements Table 3 1 Tools Palette Functions Icon Tool Name Function Selection Tool Select an ROI in the image and adjust the position of its control points and contours Action Click ROI or control points Point Select a pixel in the image K Action Click the position of the pixel Line Draw a line in the image Action Click the initial position and click again at the final position Rectangle Draw a rectangle or square in the image Action Click one corner and drag to the opposite corner Oval Draw an oval or circle in the image O Action Click the center position and drag to the required size Polygon Draw a polygon in the image Action Click to place a new vertex and double click to complete the ROI element Freehand Region Draw a freehand region in the image Action Click the initial position drag to the required shape and release the mouse button to complete the shape Annulus Draw an annulus in the image Action Click the center position and drag to the
18. and parameters to specify how the method operates at run time using CWIMAQMatchColorPatternOptions 6 Use CWIMAQVision MatchColorPattern to test the color location algorithm on test images 7 Verify the results using a ranking method Use CWIMAQVision WriteImageAndVisionInfo to save the template image Convert Pixel Coordinates to Real World Coordinates The measurement points you located with edge detection and pattern matching are in pixel coordinates If you need to make measurements using real world units use CWIMAQVision ConvertPixelToRealWorldCoordinates to convert the pixel coordinates into real world units Make Measurements You can make different types of measurements either directly from the image or from points that you detect in the image Distance Measurements Use the following methods to make distance measurements for the inspection application Clamp methods measure the separation between two edges in a rectangular search region First clamp methods detect points along the two edges using National Instruments Corporation 5 31 NI Vision for Visual Basic User Manual Chapter 5 Performing Machine Vision Tasks the rake method and then they compute the distance between the points detected on the edges along each search line of the rake and return the largest or smallest distance in either the horizontal or vertical direction The MeasurementAxis parameter specifies the axis along which to measure You
19. end points e FindAngleBetweenLines Finds the smaller angle between two lines e FindPerpendicularLine Finds the perpendicular line from a point to a line e FindDistanceFromPointToLine Computes the perpendicular distance between the point and the line e FindBisectingLine Finds the line that bisects the angle formed by two lines NI Vision for Visual Basic User Manual 5 32 ni com Chapter 5 Performing Machine Vision Tasks e FindMidLine Finds the line that is midway between a point and a line and is parallel to the line e FindPolygonArea Calculates the area of a polygon specified by its vertex points Instrument Reader Measurements You can make measurements based on the values obtained by meter LCD and barcode readers Use CWIMAQMeterArc CreateFromPoints or CWIMAQMeterArc CreateFromLines to calibrate a meter or gauge that you want to read CWIMAQMeterArc CreateFromLines calibrates the meter using the initial position and the full scale position of the needle cWIMAQMeterArc CreateFromPoints calibrates the meter using three points on the meter the base of the needle the tip of the needle at its initial position and the tip of the needle at its full scale position Use CWIMAQVision ReadMeter to read the position of the needle using the CWIMAQMeterArc object Use CWIMAQVision FindLCDSegments to calculate the regions of interest around each digit in an LCD or LED To find the area of each digit all the se
20. for Visual Basic User Manual Glossary ROI ROI tools rotational shift rotation invariant matching S saturation scale invariant matching segmentation function separation function shape detection shift invariant matching skeleton function smoothing filter Sobel filter NI Vision for Visual Basic User Manual G 16 Region of interest 1 An area of the image that is graphically selected from a window displaying the image This area can be used focus further processing 2 A hardware programmable rectangular portion of the acquisition window A collection of tools that enable you to select a region of interest from an image These tools let you select points lines annuli polygons rectangles rotated rectangles ovals and freehand open and closed contours The amount by which one image is rotated relative to a reference image This rotation is computed relative to the center of the image A pattern matching technique in which the reference pattern can be located at any orientation in the test image as well as rotated at any degree The amount of white added to a pure color Saturation relates to the richness of a color A saturation of zero corresponds to a pure color with no white added Pink is a red with low saturation A pattern matching technique in which the reference pattern can be any size in the test image Fully partitions a labeled binary image into non overlapping segments with e
21. frequency remain unaltered and the frequencies below it are removed 3 To transform the image back to the spatial domain use CWIMAQVision InverseFFT Complex Image Operations CWIMAQVision ReplaceComplexPlane and CWIMAQVision ExtractComplexPlane allow you to access process and update independently the magnitude phase real and imaginary planes of a complex image You can also convert a complex image to an array and back with CWIMAQImage ImageToArray and CWIMAQImage ArrayToImage NI Vision for Visual Basic User Manual 2 12 ni com Making Grayscale and Color Measurements This chapter describes how to take measurements from grayscale and color images You can make inspection decisions based on image statistics such as the mean intensity level in a region Based on the image statistics you can perform many machine vision inspection tasks on grayscale or color images such as detecting the presence or absence of components detecting flaws in parts and comparing a color component with a reference Figure 3 1 illustrates the basic steps involved in making grayscale and color measurements Define Regions of Interest Measure Measure Grayscale Statistics Color Statistics Figure 3 1 Steps to Taking Grayscale and Color Measurements Define Regions of Interest An ROI is an area of an image in which you want to focus the image analysis You can define an ROI interactively programmatically or with
22. from the template image and keep only those curves that are important to locate the template in the image The template image of a pacemaker is shown in Figure 5 14a Figure 5 14b shows all the curves that are found in the template image These images are comprised of curves that are good for matching such as those along the boundary of the pacemaker as well as noisy or unimportant curves such as those found around the barcode and areas of reflection in the image If you learn the template with the unimportant curves the geometric matching algorithm will treat these curves as important features that must be found within a valid match region You can make the matching process more reliable by removing these curves before learning the template Figure 5 14c shows the curves that are used to learn the template after the unimportant curves have been removed using the NI Vision Template Editor Figure 5 14 Selecting Good Features for Training a Template Once the template image is trained you can save the template to a file from within the NI Vision Template Editor Use CWIMAQVision ReadImageAndVisionInfo to read the template file within your application Refer to the NI Vision Template Editor Help for more information about training the Geometric Matching algorithm National Instruments Corporation 5 21 NI Vision for Visual Basic User Manual Chapter 5 Performing Machine Vision Tasks Setting Matching P
23. have the file extension JP2 A structure that represents a pixel and its relationship to its neighbors The relationship is specified by weighted coefficients of each neighbor A morphology operation that identifies each object in a binary image and assigns a unique pixel value to all the pixels in an object This process is useful for identifying the number of objects in the image and giving each object a unique pixel intensity Measures the distance between selected edges with high precision subpixel accuracy along a line in an image For example this function can be used to measure distances between points and edges This function also can step and repeat its measurements across the image Represents the gray level distribution along a line of pixels in an image A special algorithm that calculates the value of a pixel based on its own pixel value as well as the pixel values of its neighbors The sum of this calculation is divided by the sum of the elements in the matrix to obtain a new pixel value Creates a binary image by segmenting a grayscale image into a particle region and a background region NI Vision for Visual Basic User Manual G 10 ni com logarithmic function logic operators lossless compression lossy compression lowpass attenuation lowpass FFT filter lowpass filter lowpass frequency filter lowpass truncation L skeleton function luma luminance LUT National Instruments Corporation G 1
24. measure the width of the part You can combine multiple edge locations to compute intersection points projections circles or ellipse fits Pattern matching algorithms use edges and patterns Pattern matching can locate with very high accuracy the position of fiducials or characteristic features of the part under inspection Those locations can then be combined to compute lengths angles and other object measurements The robustness of the measurement relies on the stability of the image acquisition conditions Sensor resolution lighting optics vibration control part fixture and general environment are key components of the imaging setup All the elements of the image acquisition chain directly affect the accuracy of the measurements National Instruments Corporation 5 1 NI Vision for Visual Basic User Manual Chapter 5 3 Locate Objects to Inspect Performing Machine Vision Tasks Figure 5 1 illustrates the basic steps involved in performing machine vision Locate Objects to Inspect Set Search Areas Find Measurement Points Identify Parts Under Inspection Classify Convert Pixel Coordinates to Real World Coordinates EER re Make Measurements Display Results Objects Characters Read Barcodes Note Diagram items enclosed with dashed lines are optional steps Figure 5 1 Steps to Performing Machine Vision In a typic
25. narrow areas use connectivity 8 to ensure that the software recognizes the connected pixels as one particle If you have two particles that touch at one point use connectivity 4 to ensure that the software recognizes the pixels as two separate particles For more information about connectivity refer to Chapter 9 Binary Morphology of the NI Vision Concepts Manual 3 Note Use the same type of connectivity throughout the application Removing Unwanted Particles Use CWIMAQVision RejectBorder to remove particles that touch the border of the image Reject particles on the border of the image when you suspect that the information about those particles is incomplete Use CWIMAQVision RemoveParticle to remove large or small particles that do not interest you You also can use the Erode Open and POpen methods in CWIMAQVision Morphology to remove small particles Unlike CWIMAQVision RemoveParticle these three methods alter the size and shape of the remaining particles Use the hit miss method of CWIMAQVision Morphology to locate particular configurations of pixels which you define with a structuring element Depending on the configuration of the structuring element the hit miss method can locate single isolated pixels cross shape or longitudinal patterns right angles along the edges of particles and other user specified shapes For more information about structuring elements refer to Chapter 9 Binary Morphology of the NI Vision Concep
26. of 0 the corresponding pixel in the source image is left unchanged You can use a mask to define particles in a grayscale image when you need to make intensity measurements on those particles First threshold the image to make a new binary image For more information about binary images refer to Chapter 4 Performing Particle Analysis You can input the binary image or a labeled version of the binary image as a mask image to the intensity measurement method If you want to make color comparisons convert the binary image into a CWIMAQRegions collection using CWIMAQVision MaskToRegions Measure Grayscale Statistics You can measure grayscale statistics in images using light meters or quantitative analysis methods You can obtain the center of energy for an image with the centroid method Use CWMachineVision LightMeterPoint to measure the light intensity at a point in the image Use CWMachineVision LightMeterLine to get the pixel value statistics along a line in the image such as mean intensity standard deviation NI Vision for Visual Basic User Manual 3 6 ni com Chapter 3 Making Grayscale and Color Measurements minimum intensity and maximum intensity Use CWMachineVision LightMeterRectangle to get the pixel value statistics within a rectangular region in an image Use CWIMAQVision Quantify to obtain the following statistics about the entire image or individual regions in the image mean intensity standard deviation minimum
27. required size Adjust the inner and outer radii and adjust the start and end angle Zoom Zoom in or zoom out in an image Action Click the image to zoom in Hold down the lt Shift gt key and click to zoom out Pan Pan around an image Action Click an initial position drag to the required position and release the mouse button to complete the pan NI Vision for Visual Basic User Manual 3 2 ni com Chapter 3 Making Grayscale and Color Measurements Table 3 1 Tools Palette Functions Continued Icon Tool Name Function Draw a broken line in the image Action Click to place a new vertex and double click to complete the ROI element Freehand Line Draw a freehand line in the image Action Click the initial position drag to the required shape and release the mouse button to complete the shape Rotated Rectangle Draw a rotated rectangle in the image Action Click one corner and drag to the opposite corner to create the rectangle Then click the lines inside the rectangle and drag to adjust the rotation angle Hold down lt Shift gt when drawing an ROI if you want to constrain the ROI to the horizontal vertical or diagonal axes when possible Use the selection tool to position an ROI by its control points or vertices ROIs are context sensitive meaning that the cursor actions differ depending on the ROI with which you interact For example if you mo
28. the color information that you want to use as a reference The color information can consist of a single color or multiple dissimilar colors such as red and blue Use the entire image or regions in the image to learn the color information using CWIMAQVision LearnColor which stores the results of the operation in a CWIMAQColorInformation object that you supply as a parameter The color information object has a color spectrum that contains a compact description of the color information that you learned Refer to Chapter 15 Color Inspection of the NI Vision Concepts Manual for more information Use the CWIMAQColorInformation object to represent the learned color information for all subsequent matching operations Define an entire image a region or multiple regions in an image as the inspection or comparison area Use CWIMAQVision MatchColor to compare the learned color information to the color information in the inspection regions This method returns an array of scores that indicates how close the matches are to the learned color information Use the color matching score as a measure of similarity between the reference color information and the color information in the image regions being compared Learning Color Information When learning color information choose the color information carefully National Instruments Corporation Specify an image or regions in an image that contain the color or color set that you want to learn
29. value is obtained from the lookup table NI Vision for Visual Basic User Manual Glossary machine vision mask FFT filter match score MB median filter memory buffer MMX morphological transformations M skeleton function multiple template matching 1 Mega the standard metric prefix for 1 million or 106 when used with units of measure such as volts and hertz 2 Mega the prefix for 1 048 576 or 220 when used with B to quantify data or computer memory An automated application that performs a set of visual inspection tasks Removes frequencies contained in a mask range specified by the user A number ranging from 0 to 1000 that indicates how closely an acquired image matches the template image A match score of 1000 indicates a perfect match A match score of 0 indicates no match Megabyte of memory A lowpass filter that assigns to each pixel the median value of its neighbors This filter effectively removes isolated pixels without blurring the contours of objects See buffer Multimedia Extensions An Intel chip based technology that allows parallel operations on integers which results in accelerated processing of 8 bit images Extract and alter the structure of objects in an image You can use these transformations for expanding dilating or reducing eroding objects filling holes closing inclusions or smoothing borders They are used primarily to delineate objects and prepare them for qua
30. want the viewer to display When you attach an image to a viewer the image automatically updates the viewer whenever an operation modifies the contents of the image You can access the image attached to the viewer using the CWIMAQViewer Image property Before you attach an image to the viewer the viewer already has an image attached by default Therefore the viewer has an image attached to it at all times You can use the attached image as either a source image destination image or both using the CWIMAQViewer Image property You can use the CWIMAQViewer Palette property to access the CWIMAQPalette object associated with the viewer Use the CWIMAQPalette object to programmatically apply a color palette to NI Vision for Visual Basic User Manual 2 6 ni com Chapter 2 Getting Measurement Ready Images the viewer You can set the CWIMAQPalette Type property to apply predefined color palettes For example if you need to display a binary image an image that contains particle regions with pixel values of 1 and a background region with pixel values of 0 set the Type property to cwimagPaletteBinary For more information about color palettes refer to Chapter 2 Display of the NI Vision Concepts Manual You also can set a default palette during design time using the Menu property page Users can change the color palette during run time by using the right click menu on the viewer Attach Calibration Information 3 If you want t
31. where the image is sorted within a discrete number of classes corresponding to the number of phases perceived in an image The gray values and a barycenter are determined for each class This process is repeated until a value is obtained that represents the center of mass for each phase or class Color lookup table A table for converting the value of a pixel in an image into a red green and blue RGB intensity An image containing color information usually encoded in the RGB form The mathematical representation for a color For example color can be described in terms of red green and blue hue saturation and luminance or hue saturation and intensity Stores information obtained from the FFT of an image The complex numbers that compose the FFT plane are encoded in 64 bit floating point values 32 bits for the real part and 32 bits for the imaginary part National Instruments Corporation G 3 NI Vision for Visual Basic User Manual Glossary connectivity connectivity 4 connectivity 8 contrast convex hull convex hull function convolution convolution kernel curve extraction D Danielsson function determinism digital image dilation driver Defines which of the surrounding pixels of a given pixel constitute its neighborhood Only pixels adjacent in the horizontal and vertical directions are considered neighbors All adjacent pixels are considered neighbors A constant multiplication fact
32. within a continuous range of intensities and you can specify this threshold range manually use CWIMAQVision Threshold to threshold the image National Instruments Corporation 4 1 NI Vision for Visual Basic User Manual Chapter 4 Performing Particle Analysis If all the objects in the grayscale image are either brighter or darker than the background you can use CWIMAQVision AutoThreshold2 to automatically determine the optimal threshold range and threshold the image Automatic thresholding techniques offer more flexibility than simple thresholds based on fixed ranges Because automatic thresholding techniques determine the threshold level according to the image histogram the operation is more independent of changes in the overall brightness and contrast of the image than a fixed threshold These techniques are more resistant to changes in lighting which makes them well suited for automated inspection tasks If the grayscale image contains objects that have multiple discontinuous grayscale values use CWIMAQVision MultiThreshold2 to specify multiple threshold ranges If you need to threshold a grayscale image that has nonuniform lighting conditions such as those resulting from a strong illumination gradient or shadows use CWIMAQVision LocalThreshold You need to define a pixel window that specifies which neighboring pixels are considered in the statistical calculation The default window size is 32 x 32 However the window size should b
33. 0 speed of the pattern matching algorithm 5 18 inspection defects 5 35 instrument drivers NI resources A 1 instrument reader measurements 5 33 interactively defining regions 5 8 K KnowledgeBase A 1 L learning calibration information 6 5 color information 3 9 color spectrums 3 10 colors 3 12 correction tables 6 8 error maps 6 8 learning algorithm specifying 6 6 learning calibration information correction tables 6 8 error maps 6 8 setting the scaling mode 6 8 specifying a learning algorithm 6 6 specifying a region of interest 6 6 specifying scaling factors 6 6 using learning scores 6 7 voiding calibrations 6 9 learning score using 6 7 light intensity measuring 3 6 lighting effects on image colors 3 11 NI Vision for Visual Basic User Manual l 4 lines finding 5 9 local threshold 4 2 locating objects to detect 5 2 lowpass attenuation 2 12 filter 2 9 LUTs highlighting details in images 2 9 machine vision 5 1 masks defining regions of interest 3 6 measurement points finding 5 9 measurements distance 5 31 geometry 5 32 instrument reader 5 33 measuring color statistics 3 6 3 7 grayscale statistics 3 6 images 5 31 light intensity 3 6 particles 4 5 measuring particles 4 5 method for building coordinate transformations 5 7 multiple ROIs using to view color differences in an image 3 11 multiple search contours finding edge points 5 12 multiple template images
34. 1 Glossary Increases the brightness and contrast in dark regions of an image and decreases the contrast in bright regions of the image The image operations AND NAND OR XOR NOR XNOR difference mask mean max and min Compression in which the decompressed image is identical to the original image Compression in which the decompressed image is visually similar but not identical to the original image Applies a linear attenuation to the frequencies in an image with no attenuation at the lowest frequency and full attenuation at the highest frequency Removes or attenuates high frequencies present in the FFT domain of an image Attenuates intensity variations in an image You can use these filters to smooth an image by eliminating fine details and blurring edges Attenuates high frequencies present in the frequency domain of the image A lowpass frequency filter suppresses information related to fast variations of light intensities in the spatial image Removes all frequency information above a certain frequency Uses an L shaped structuring element in the skeleton function The brightness information in the video picture The luma signal amplitude varies in proportion to the brightness of the video signal and corresponds exactly to the monochrome picture See luma Lookup table A table containing values used to transform the gray level values of an image For each gray level value in the image the corresponding new
35. 5 23 multiple threshold 4 2 National Instruments support and services A 1 NI Vision applications creating 1 4 NI Vision Template Editor 5 21 Niblack algorithm 4 2 NI IMAQ xi ni com niocr ocx 1 2 nonlinear calibration 6 1 Nth order filter 2 10 0 objects classifying 5 34 detecting 5 2 locating 5 2 OCR 5 34 one shot acquisition 2 4 optimizing speed of the color pattern matching algorithm 5 28 P particle analysis 4 1 improving binary image separating touching particles 4 4 performing 4 1 particle measurements 4 5 particle shapes improving 4 4 particles measuring 4 5 removing unwanted 4 3 pattern matching building a coordinate transformation 5 5 finding points 5 12 score 5 29 setting rotation angle ranges 5 18 setting tolerances 5 17 5 28 tolerances setting 5 28 training algorithm 5 15 verifying results 5 19 pattern matching algorithms using contrast 5 18 PDF417 barcodes reading 5 35 perspective calibration 6 1 pixel coordinates converting to real world coordinates 5 31 National Instruments Corporation l 5 Index points finding along one search contour 5 11 finding along the edge of a circle 5 10 finding measurement points 5 9 finding with color location 5 30 finding with color pattern matching 5 24 finding with geometric matching 5 19 finding with pattern matching 5 12 programmatically defining regions 5 9 regions of interest 3 5 programming exam
36. A binary mask used in most morphological operations A structuring element is used to determine which neighboring pixels contribute in the operation Finds the location of the edge coordinates in terms of fractions of a pixel A color shape or pattern that you are trying to match in an image using the color matching shape matching or pattern matching functions A template can be a region selected from an image or it can be an entire image Separates objects from the background by assigning all pixels with intensities within a specified range to the object and the rest of the pixels to the background In the resulting binary image objects are represented with a pixel intensity of 255 and the background is set to 0 Two parameters the lower threshold gray level value and the upper threshold gray level value Tagged Image File Format An image format commonly used for encoding 8 bit 16 bit and color images TIFF images have the file extension TIF Describes algorithms that are designed to support a lower and upper bound on execution time A collection of tools that enable you to select regions of interest zoom in and out and change the image palette G 17 NI Vision for Visual Basic User Manual Glossary V value W Watershed Transform The grayscale intensity of a color pixel computed as the average of the maximum and minimum red green and blue values of that pixel Partitions an image based on the topographic surf
37. Basic User Manual Contents Grayscale Morphology ceeeeseeesseceseeseeeseeseeseeeseeseeeaeceeeeaeseaeeaeeeeeneesaees 2 10 PE E E T E distseee ta bnshe Mundealsauicdas hitachi 2 11 Complex Image Operations 0 eee ec eeceeseesceesecseceeeseeeeseeseeeseeaeees 2 12 Chapter 3 Making Grayscale and Color Measurements Define R gions of Interest c c25 5 zsiceissescescessacssigcaass aa aa a ai 3 1 Defining Regions Interactively eseeesseeseseeseersrrsrsresteersresresrsresresesreseseesteee 3 1 Defining Regions Programmatically eee eeeseeeeeseeeeeeseeeseeseeeeeeseenees 3 5 Defining Regions with Masks 00 0 ei eeeesesseeeseeseeeseeeeeseeseeeseceeesesseseaeesees 3 6 Measure Grayscale Statistics 0 0 eee en eena ea ii R ESEE R ARRE 3 6 Measure Color Statisties rosei n Sea es r a ieth at 3 7 Comparing Colors enna eA E A EA 3 9 Learning Color Information sseesssseessessseeseesrsrtstsrrssestsestesrererrsrestesrsresresent 3 9 Specifying the Color Information to Learn eee eee eeeeeeeeeeees 3 10 Using the Entire Imagenes nieri na a beet 3 10 Choosing a Color Representation Sensitivity eee eee 3 12 Tgnoring Learned Colors rirerire aaepe i iE 3 13 Chapter 4 Performing Particle Analysis Create a Binary Image eeir e a e a E A a O ETE 4 1 Improveth Binary IMa E aien nnie EE EE be A e a aa 4 2 Removing Unwanted Particles eeeeseseeeseeseeeseeseceseeseesseceseesesseseaeesees 4 3 Separating Touching Part
38. Dimensional Measurements of the NI Vision Concepts Manual You can build a coordinate transformation using edge detection or pattern matching The output of the edge detection and pattern matching methods that build a coordinate transformation is a CWMvVCoordinateTransformation object which contains a reference coordinate system and a measurement coordinate system Some machine vision methods take this transformation and adjust the regions of inspection automatically You also can use these outputs to move the regions of inspection relative to the object programmatically Using Edge Detection to Build a Coordinate Transformation You can build a coordinate transformation using two edge detection techniques Use cWMachineVision FindCoordTransformUsingRect to define a reference coordinate system using one rectangular region Use CWMachineVision FindCoordTransformUsingTwoRects to define a reference coordinate system using two independent rectangular regions Follow the steps below to build a coordinate transformation using edge detection ay Note To use this technique the object cannot rotate more than 65 in the image 1 Specify one or two rectangular ROIs a Ifyou use CWMachineVision FindCoordTransformUsingRect specify one rectangular ROI that includes part of two straight nonparallel boundaries of the object as shown in Figure 5 2 This rectangular region must be large enough to include these boundaries in all the images you w
39. NI Vision NI Vision for Visual Basic User Manual July 2007 7 NATIONAL 371257C 01 INSTRUMENTS Worldwide Technical Support and Product Information ni com National Instruments Corporate Headquarters 11500 North Mopac Expressway Austin Texas 78759 3504 USA Tel 512 683 0100 Worldwide Offices Australia 1800 300 800 Austria 43 662 457990 0 Belgium 32 0 2 757 0020 Brazil 55 11 3262 3599 Canada 800 433 3488 China 86 21 5050 9800 Czech Republic 420 224 235 774 Denmark 45 45 76 26 00 Finland 358 0 9 725 72511 France 01 57 66 24 24 Germany 49 89 7413130 India 91 80 41190000 Israel 972 3 6393737 Italy 39 02 413091 Japan 81 3 5472 2970 Korea 82 02 3451 3400 Lebanon 961 0 1 33 28 28 Malaysia 1800 887710 Mexico 01 800 010 0793 Netherlands 31 0 348 433 466 New Zealand 0800 553 322 Norway 47 0 66 90 76 60 Poland 48 22 3390150 Portugal 351 210 311 210 Russia 7 495 783 6851 Singapore 1800 226 5886 Slovenia 386 3 425 42 00 South Africa 27 0 11 805 8197 Spain 34 91 640 0085 Sweden 46 0 8 587 895 00 Switzerland 41 56 2005151 Taiwan 886 02 2377 2222 Thailand 662 278 6777 Turkey 90 212 279 3031 United Kingdom 44 0 1635 523545 For further support information refer to the Technical Support and Professional Services appendix To comment on National Instruments documentation refer to the National Instruments Web site at ni com info and enter the info code feedback 2001 2007 National Instruments Corporatio
40. OM NATIONAL INSTRUMENTS TESTING PLATFORMS AND BECAUSE A USER OR APPLICATION DESIGNER MAY USE NATIONAL INSTRUMENTS PRODUCTS IN COMBINATION WITH OTHER PRODUCTS IN A MANNER NOT EVALUATED OR CONTEMPLATED BY NATIONAL INSTRUMENTS THE USER OR APPLICATION DESIGNER IS ULTIMATELY RESPONSIBLE FOR VERIFYING AND VALIDATING THE SUITABILITY OF NATIONAL INSTRUMENTS PRODUCTS WHENEVER NATIONAL INSTRUMENTS PRODUCTS ARE INCORPORATED IN A SYSTEM OR APPLICATION INCLUDING WITHOUT LIMITATION THE APPROPRIATE DESIGN PROCESS AND SAFETY LEVEL OF SUCH SYSTEM OR APPLICATION Contents About This Manual GOMVENEIONS 5555 bs os Sees ati aces asi coved eed ea Le ses ad eee a aed 1X Related DoCUMentatiONn 0 ccccccccccececessssesssesessscscscseeescscsccecececeesesesesssesesescsseseesseseeess x Chapter 1 Introduction to NI Vision ADOUEINT 6s tech tes ceess eins cn TE Reisen ATRIA ota oe Sa eed ea 1 1 NI Vision for Visual Basic Organization ee eee eeceeseceseeseeeseeseeesecseeeaeeneeesseneeaes 1 1 CWIMAG OCK descend cians Secdvacue E satan sudees RE A E A a tighausa S 1 1 CWIMAQ Control isrann hides neck seths atin eee ee thes 1 2 CWIMAOQVisiION Control ccccceesccceesceeeseeessneeesseeeeseeeeseneeesseeens 1 2 CWIMAQViewer Control ccccceescccesssceeenseeessneeesseeeeseeeessneeesseeens 1 2 NIOCTIOCK se seth he A esti ns hs eis ae et i inlet 1 2 NIOGR Controlscs2 testis sce iedcn E A RRR 1 3 CWO R occ cdedes caus ivedes even thes eave dts u
41. S AND TESTING FOR A LEVEL OF RELIABILITY SUITABLE FOR USE IN OR IN CONNECTION WITH SURGICAL IMPLANTS OR AS CRITICAL COMPONENTS IN ANY LIFE SUPPORT SYSTEMS WHOSE FAILURE TO PERFORM CAN REASONABLY BE EXPECTED TO CAUSE SIGNIFICANT INJURY TO A HUMAN 2 IN ANY APPLICATION INCLUDING THE ABOVE RELIABILITY OF OPERATION OF THE SOFTWARE PRODUCTS CAN BE IMPAIRED BY ADVERSE FACTORS INCLUDING BUT NOT LIMITED TO FLUCTUATIONS IN ELECTRICAL POWER SUPPLY COMPUTER HARDWARE MALFUNCTIONS COMPUTER OPERATING SYSTEM SOFTWARE FITNESS FITNESS OF COMPILERS AND DEVELOPMENT SOFTWARE USED TO DEVELOP AN APPLICATION INSTALLATION ERRORS SOFTWARE AND HARDWARE COMPATIBILITY PROBLEMS MALFUNCTIONS OR FAILURES OF ELECTRONIC MONITORING OR CONTROL DEVICES TRANSIENT FAILURES OF ELECTRONIC SYSTEMS HARDWARE AND OR SOFTWARE UNANTICIPATED USES OR MISUSES OR ERRORS ON THE PART OF THE USER OR APPLICATIONS DESIGNER ADVERSE FACTORS SUCH AS THESE ARE HEREAFTER COLLECTIVELY TERMED SYSTEM FAILURES ANY APPLICATION WHERE A SYSTEM FAILURE WOULD CREATE A RISK OF HARM TO PROPERTY OR PERSONS INCLUDING THE RISK OF BODILY INJURY AND DEATH SHOULD NOT BE RELIANT SOLELY UPON ONE FORM OF ELECTRONIC SYSTEM DUE TO THE RISK OF SYSTEM FAILURE TO AVOID DAMAGE INJURY OR DEATH THE USER OR APPLICATION DESIGNER MUST TAKE REASONABLY PRUDENT STEPS TO PROTECT AGAINST SYSTEM FAILURES INCLUDING BUT NOT LIMITED TO BACK UP OR SHUT DOWN MECHANISMS BECAUSE EACH END USER SYSTEM IS CUSTOMIZED AND DIFFERS FR
42. Vision LineProfile 2 to get the pixel distribution along a line in the image or use CWIMAQVision RegionsProfile to get the pixel distribution along a one dimensional path in the image By looking at the pixel distribution you can determine if the image quality is high enough to provide you with sharp edges at object boundaries Also you can determine if the image is noisy and identify the characteristics of the noise If the image quality meets your needs use the pixel distribution information to determine some parameters of the inspection methods you want to use For example use the information from the line profile to determine the strength of the edge at the boundary of an object You can input this information into CWIMAQVision FindEdges2 to find the edges of objects along the line NI Vision for Visual Basic User Manual 2 8 ni com Chapter 2 Getting Measurement Ready Images Improve an Image Using the information you gathered from analyzing the image you may want to improve the quality of the image for inspection You can improve the image with lookup tables filters grayscale morphology and Fast Fourier transforms FFT Lookup Tables Apply lookup table LUT transformations to highlight image details in areas containing significant information at the expense of other areas A LUT transformation converts input grayscale values in the source image into other grayscale values in the transformed image NI Vision provides fou
43. WIMAQSimpleCalibrationOptions CalibrationAxisInfo to define the coordinate reference Use CWIMAQSimpleCalibrationOptions GridDescriptor to specify the scaling factors Use CWIMAQSimpleCalibrationOptions CorrectionScalingMode to set the scaling mode Set CWIMAQSimpleCalibrationOptions LearnCorrectionTable to True to learn the correction table National Instruments Corporation 6 9 NI Vision for Visual Basic User Manual Chapter 6 Calibrating Images 1 Origin Figure 6 7 Defining a Simple Calibration Save Calibration Information After you learn the calibration information you can save it so that you do not have to relearn the information for subsequent processing Use CWIMAQVision WriteImageAndVisionInfo to save the image of the grid and its associated calibration information to a file To read the file containing the calibration information use CWIMAQVision ReadImageAndVisionInfo For more information about attaching the calibration information you read from another image refer to the Attach Calibration Information section Attach Calibration Information When you finish calibrating the setup you can apply the calibration settings to images that you acquire Use CWIMAQVision SetCalibrationInformation 2 to attach the calibration information of the current setup to each image you acquire This method takes in a source image containing the calibration information and a destination image that you wa
44. a coordinate transformation using pattern matching Use CWMachineVision FindCoordTransformUsingPattern to define a reference coordinate system based on the location of a reference feature Use this technique when the object under inspection does not have straight distinct edges Follow the steps below to build a coordinate transformation using pattern matching 3 Note The object may rotate 360 in the image using this technique if you use rotation invariant pattern matching National Instruments Corporation 5 5 NI Vision for Visual Basic User Manual Chapter 5 Performing Machine Vision Tasks 1 Define a template that represents the part of the object that you want to use as a reference feature For more information about defining a template refer to the Find Measurement Points section 2 Define a rectangular search area in which you expect to find the template 3 Set the MatchMode property of the CWMVFindCTUsingPatternOptions object to cwimagRotationInvariant when you expect the template to appear rotated in the inspection images Otherwise set it to cwimaqShiftInvariant 4 Choose the results you want to overlay onto the image Choose the mode for the method To build a transformation for the first time set the FirstRun parameter to True To update the transformation in subsequent images set this parameter to False NI Vision for Visual Basic User Manual 5 6 ni com Chapter 5 Performing Machine Vision Tasks Choosing a
45. ace of the image The image is separated into non overlapping segments with each segment containing a unique particle NI Vision for Visual Basic User Manual G 18 ni com Index Numerics 1D barcodes reading 5 34 A acquiring images 2 4 continuous acquisition 2 5 one shot acquisition 2 4 Acquisition Type combo box 2 4 ActiveX objects 1 4 adding shapes to ROIs 3 5 analyzing images 2 7 2 8 Application 1 5 application development general steps 1 5 inspection steps 1 6 arrays converting to images 2 6 attaching calibration information to images 2 7 6 10 attenuation highpass 2 12 lowpass 2 12 automatic threshold 4 2 background correction algorithm 4 2 barcodes reading 5 34 reading 1D 5 34 reading data matrix barcodes 5 35 reading PDF417 barcodes 5 35 binary images improving 4 2 binary morphology 4 4 building coordinate transformation with edge detection 5 3 National Instruments Corporation coordinate transformation with pattern matching 5 5 building coordinate transformations 5 7 choosing a method 5 7 C calibrating images 2 2 6 1 calibration nonlinear 6 1 perspective 6 1 voiding 6 9 calibration information 6 5 attaching to images 2 7 6 10 saving 6 10 calibration templates defining 6 2 center of energy obtaining with centroid method 3 6 centroid method 3 6 character verification 5 36 characters reading 5 34 training 5 34 circle finding points along the edg
46. ach segment containing a unique object Separates objects that touch each other by narrow isthmuses Detects rectangles lines ellipses and circles within images A pattern matching technique in which the reference pattern can be located anywhere in the test image but cannot be rotated or scaled Applies a succession of thinning operations to an object until its width becomes one pixel Blurs an image by attenuating variations of light intensity in the neighborhood of a pixel An edge detection algorithm that extracts the contours in gray level values using a 3 x 3 filter kernel ni com spatial calibration spatial filters spatial resolution square function square root function standard representation structuring element subpixel analysis T template threshold threshold interval TIFF time bounded tools palette National Instruments Corporation Glossary Assigns physical dimensions to the area of a pixel in an image Alter the intensity of a pixel relative to variations in intensities of its neighboring pixels You can use these filters for edge detection image enhancement noise reduction smoothing and so forth The number of pixels in an image in terms of the number of rows and columns in the image See exponential function See logarithmic function Contains the low frequency information at the corners and high frequency information at the center of an FFT transformed image
47. al machine vision application you extract measurements from regions of interest rather than the entire image To use this technique the parts of the object you are interested in must always appear inside the regions of interest you define If the object under inspection is always at the same location and orientation in the images you need to process defining regions of interest is simple Refer to the Set Search Areas section of this chapter for information about selecting a region of interest NI Vision for Visual Basic User Manual 5 2 ni com Chapter 5 Performing Machine Vision Tasks Often the object under inspection appears rotated or shifted in the image you need to process with respect to the reference image in which you located the object When this occurs the ROIs must shift and rotate with the parts of the object in which you are interested For the ROIs to move with the object you must define a reference coordinate system relative to the object in the reference image During the measurement process the coordinate system moves with the object when it appears shifted and rotated in the image you need to process This coordinate system is referred to as the measurement coordinate system The measurement methods automatically move the ROIs to the correct position using the position of the measurement coordinate system with respect to the reference coordinate system For information about coordinate systems refer to Chapter 14
48. an image Figure 3 7 shows an example of using a region that contains the color information that is important for the application Figure 3 7 Using a Single Region to Learn Color Distribution Using Multiple Regions in the Image The interaction of light with the object surface creates the observed color of that object The color of a surface depends on the directions of illumination and the direction from which the surface is observed Two identical objects may have different appearances because of a difference in positioning or a change in the lighting conditions Figure 3 8 shows how light reflects differently off of the 3D surfaces of the fuses resulting in slightly different colors for identical fuses To view the color differences compare the 3 amp fuse in the upper row with the 3 amp fuse in the lower row If you learn the color spectrum by drawing a region of interest around the 3 amp fuse in the upper row and then do a color matching for the 3 amp fuse in the upper row you get a very high match score for it close to 1000 The match score for the 3 amp fuse in the lower row is low around 500 This problem could cause a mismatch for the color matching in a fuse box inspection process The color learning functionality of NI Vision uses a clustering process to find the representative colors from the color information specified by one or multiple regions in the image To create a representative color spectrum for all 3 amp fuse
49. and the pattern that you want to find make sure that all the important and unique characteristics of the pattern are well defined in the image Several factors are critical in creating a template image These critical factors include color information symmetry feature detail positional information and background information Color Information A template with colors that are unique to the pattern provides better results than a template that contains many colors especially colors found in the background or other objects in the image Symmetry A rotationally symmetric template in the luminance plane is less sensitive to changes in rotation than one that is rotationally asymmetric Feature Detail A template with relatively coarse features is less sensitive to variations in size and rotation than a model with fine features However the model must contain enough detail to identify it Positional Information A template with strong edges in both the x and y directions is easier to locate National Instruments Corporation 5 25 NI Vision for Visual Basic User Manual Chapter 5 Performing Machine Vision Tasks Background Information Unique background information in a template improves search performance and accuracy during the grayscale pattern matching phase This requirement could conflict with the color information requirement because background colors may not be appropriate during the color location phase Avoid t
50. ant to inspect National Instruments Corporation 5 3 NI Vision for Visual Basic User Manual Chapter 5 Performing Machine Vision Tasks of b 1 Search Area for the Coordinate System 3 Origin of the Coordinate System 2 Object Edges 4 Measurement Area Figure 5 2 Coordinate Systems of a Reference Image and Inspection Image b If you use CWMachineVision FindCoordTransformUsingTwoRects specify two rectangular ROIs each containing one separate straight boundary of the object as shown in Figure 5 3 The boundaries cannot be parallel The regions must be large enough to include the boundaries in all of the images you want to inspect NI Vision for Visual Basic User Manual 5 4 ni com Chapter 5 Performing Machine Vision Tasks 1 Primary Search Area 3 Origin of the Coordinate System 2 Secondary Search Area 4 Measurement Area Figure 5 3 Locating Coordinate System Axes with Two Search Areas Choose the parameters you need to locate the edges on the object Choose the coordinate system axis direction Choose the results that you want to overlay onto the image WP eo Choose the mode for the method To build a coordinate transformation for the first time set the FirstRun parameter to True To update the coordinate transformation in subsequent images set this parameter to False Using Pattern Matching to Build a Coordinate Transformation You can build
51. arameters and Tolerances The geometric matching algorithm makes assumptions about the images and geometric matching parameters used in machine vision applications These assumptions work for a high percentage of the applications However there may be applications in which the assumptions used in the algorithm are not optimal Knowing your particular application and the images you want to process is useful in selecting the pattern matching parameters The following sections discuss parameters that influence the NI Vision Geometric Matching algorithm Match Mode Set the match mode to control the conditions under which the geometric matching algorithm finds the template matches If you expect the orientation of the valid matches to vary more than 5 from the template add cwimaqGeometricMatchRotationInvariant to CWIMAQMatchGeometricPatternOptions MatchMode to enable rotation invariant matching If you expect the size of the valid matches to change more than 5 add cwimagGeometricMatchScaleInvariant to CWIMAQMatchGeometricPatternOptions MatchMode to enable scale invariant matching If you expect the valid matches to be partially covered or missing add cwimaqGeometricMatchOcclusionInvariant to CWIMAQMatchGeometricPatternOptions MatchMode to enable occlusion invariant matching Disabling any of these options or limiting their ranges decreases the search time Rotation Angle Ranges If you know the pattern is restricted to a certain rotatio
52. asic User Manual Glossary barycenter binary image binary morphology binary threshold bit depth blurring BMP border function brightness buffer Bit One binary digit either 0 or 1 Byte Eight related bits of data an eight bit binary number Also denotes the amount of memory required to store one byte of data The grayscale value representing the centroid of the range of an image s grayscale values in the image histogram An image in which the objects usually have a pixel intensity of 1 or 255 and the background has a pixel intensity of 0 Functions that perform morphological operations on a binary image The separation of an image into objects of interest assigned a pixel value of 1 and background assigned pixel values of 0 based on the intensities of the image pixels The number of bits n used to encode the value of a pixel For a given n a pixel can take 2 different values For example if n equals 8 a pixel can take 256 different values ranging from 0 to 255 If n equals 16 a pixel can take 65 536 different values ranging from 0 to 65 535 or 32 768 to 32 767 Reduces the amount of detail in an image Blurring commonly occurs because the camera is out of focus You can blur an image intentionally by applying a lowpass frequency filter Bitmap An image file format commonly used for 8 bit and color images BMP images have the file extension BMP Removes objects or particles in a
53. at you expect to perform If you do not expect the instance of the template in the image to rotate or change its size the pattern matching algorithm has to learn only those features from the template that are necessary for shift invariant matching However if you want to match the template at any orientation the learning mode must consider the possibility of arbitrary orientations To specify which type of learning mode to use pass the learn mode to the LearnPatternOptions parameter of CWIMAQVision LearnPattern2 You also can set the LearnMode property of aCWIMAQLearnPatternOptions object and pass this object for the LearnPatternOptions parameter of CWIMAQVision LearnPattern2 The learning process is usually time intensive because the algorithm attempts to find unique features of the template that allow for fast accurate matching You also can save time by training the pattern matching algorithm offline and then saving the template image with CWIMAQVision WriteImageAndVisionInfo National Instruments Corporation 5 15 NI Vision for Visual Basic User Manual Chapter 5 Performing Machine Vision Tasks Defining a Search Area Two equally important factors define the success of a pattern matching algorithm accuracy and speed You can define a search area to reduce ambiguity in the search process For example if the image has multiple instances of a pattern and only one of them is required for the inspection task the presence of ad
54. ation that separates particles without modifying their shapes However erosion and open operations alter the shape of all the particles Note A separation is a time intensive operation compared to watershed transform erosion or an open operation Consider using one of the methods other than separation if speed is an issue in your application Improving Particle Shapes Use CWIMAQVision FillHol1e to fill holes in the particles Use CWIMAQVision Morphology to perform a variety of operations on the particles You can use the Open Close Proper Open Proper Close and auto median operations to smooth the boundaries of the particles Open and Proper Open Smooth the boundaries of the particle by removing small isthmuses while close widens the isthmuses Close and Proper Close fill small holes in the particle Auto median removes isthmuses and fills holes For more information about these operations refer to Chapter 9 Binary Morphology of the NI Vision Concepts Manual NI Vision for Visual Basic User Manual 4 4 ni com Chapter 4 Performing Particle Analysis Make Particle Measurements After you create a binary image and improve it you can make particle measurements With these measurements you can determine the location of particles and their shape features Use the following methods to perform particle measurements e CWIMAQVision ParticleReport tThis method returns a CWIMAOQParticleReport object which contains for each particle
55. ations include quality of parts analyzing defects locating objects and sorting objects A set of high level software functions such as NI IMAQ that control specific plug in computer boards Instrument drivers are available in several forms ranging from a function callable from a programming language to a VI in LabVIEW The sum of the Red Green and Blue primary colors divided by three Red Green Blue 3 Assigns user defined quantities such as optical densities or concentrations to the gray level values in an image The gray level distribution of the pixels along an ROI in an image Defines the range of gray level values in an object of an image Characterizes an object based on the range of gray level values in the object If the intensity range of the object falls within the user specified range it is considered an object Otherwise it is considered part of the background NI Vision for Visual Basic User Manual Glossary jitter JPEG JPEG2000 K kernel L labeling line gauge line profile linear filter local threshold The maximum amount of time that the execution of an algorithm varies from one execution to the next Joint Photographic Experts Group An image file format for storing 8 bit and color images with lossy compression JPEG images have the file extension JPG An Image file format for storing 8 bit 16 bit or color images with either lossy or lossless compression JPEG2000 images
56. ber Mate ca ten Sane a dadcaa oa a a acbree O a elece 1 3 CWMachineVision Control cccccccesscecessecesseeesseeeeseneeesseeeesseeens 1 3 ActiveX Objects menia sch ene E las cede EA EE 1 4 Creating NI Vision Applications siisii inai eiia iai a a ais 1 4 Chapter 2 Getting Measurement Ready Images Set Up Your Im sing System snn ier eener ie EEE E EOR REER 2 1 Calibrate Your Imaging System esesssssesseesseersesrsresrsstsrestsetstestsestesrnresresrnresreersestsees 2 2 Create any Ma Ge esses esas r ash cevta ees duces E EE E EAE E 2 2 Acquire or Read an Images ive viccesesevicectevestacceveatesieverecves caves EE EET a ARREA 2 4 ACQUITING AN Imate ine tices a N a essa tans etheedoavt E EE ENE 2 4 One Shot Acquisition eienen sero era r ESAE EEE as 2 4 Continuous AcguisSitioi eskera ea e rE enS 2 5 Reading a Files ana e a es ee E 2 6 Converting an Array to an mage e ssesseeeesesreessseesresreresrerrsresrrestrsesresreseetse 2 6 Display an Image mican aa Maen a ae lea E NN a ee eda 2 6 Attach Calibration Informat ON nesese niee ieee EEEo PES e S o TERE AaS rb aeie 2 7 Atialyze an Imagen eiaa EE rE a EE AAE E OE E a a aN ae 2 7 Improve a IM age neven e E E R an SE 2 9 Lookup Tables is namra a e MA A aai 2 9 PIMC Crs arr eo A a T ER EA ea RE E EE A aE 2 9 Convolution Filtern ni A Sn vale esi ae a 2 10 Nth Order Filters ic cssiseessoprectesiee a aie ents 2 10 National Instruments Corporation v NI Vision for Visual
57. binary image that touch the image border 1 A constant added to the red green and blue components of a color pixel during the color decoding process 2 The perception by which white objects are distinguished from gray and light objects from dark objects Temporary storage for acquired data NI Vision for Visual Basic User Manual G 2 ni com C caliper center of mass chroma chromaticity closing clustering CLUT color image color space complex image Glossary 1 A function in the NI Vision Assistant and in NI Vision Builder for Automated Inspection that calculates distances angles circular fits and the center of mass based on positions given by edge detection particle analysis centroid and search functions 2 A measurement function that finds edge pairs along a specified path in the image This function performs an edge extraction and then finds edge pairs based on specified criteria such as the distance between the leading and trailing edges edge contrasts and so forth The point on an object where all the mass of the object could be concentrated without changing the first moment of the object about any axis The color information in a video signal The combination of hue and saturation The relationship between chromaticity and brightness characterizes a color A dilation followed by an erosion A closing fills small holes in objects and smooths the boundaries of objects A technique
58. c User Manual 4 10 The sum of all Y coordinates squared in the particle ni com Chapter 4 Performing Particle Analysis Table 4 1 Measurement Types Continued Measurement Description cwimaqMeasurementSumY Y Y The sum of all Y coordinates cubed in the particle cwimaqMeasurementTypesFactor Factor relating area to moment of inertia cwimaqMeasurementWaddelDiskDiameter Diameter of a disk with the same area as the particle National Instruments Corporation 4 11 NI Vision for Visual Basic User Manual Performing Machine Vision Tasks This chapter describes how to perform many common machine vision inspection tasks The most common inspection tasks are detecting the presence or absence of parts in an image and measuring the dimensions of parts to see if they meet specifications Measurements are based on characteristic features of the object represented in the image Image processing algorithms traditionally classify the type of information contained in an image as edges surfaces and textures or patterns Different types of machine vision algorithms leverage and extract one or more types of information Edge detectors and derivative techniques such as rakes concentric rakes and spokes use edges represented in the image They locate with high accuracy the position of the edge of an object in the image For example you can a technique called clamping which uses the edge location to
59. chanical including photocopying recording storing in an information retrieval system or translating in whole or in part without the prior written consent of National Instruments Corporation National Instruments respects the intellectual property of others and we ask our users to do the same NI software is protected by copyright and other intellectual property laws Where NI software may be used to reproduce software or other materials belonging to others you may use NI software only to reproduce materials that you may reproduce in accordance with the terms of any applicable license or other legal restriction Trademarks National Instruments NI ni com and LabVIEW are trademarks of National Instruments Corporation Refer to the Terms of Use section on ni com legal for more information about National Instruments trademarks Other product and company names mentioned herein are trademarks or trade names of their respective companies Members of the National Instruments Alliance Partner Program are business entities independent from National Instruments and have no agency partnership or joint venture relationship with National Instruments Patents For patents covering National Instruments products refer to the appropriate location Help Patents in your software the patents txt file on your CD or ni com patents WARNING REGARDING USE OF NATIONAL INSTRUMENTS PRODUCTS 1 NATIONAL INSTRUMENTS PRODUCTS ARE NOT DESIGNED WITH COMPONENT
60. d Tolerances Every pattern matching algorithm makes assumptions about the images and pattern matching parameters used in machine vision applications These assumptions work for a high percentage of the applications However there may be applications in which the assumptions used in the algorithm are not optimal To efficiently select the best pattern matching parameters for the application you must have a clear understanding of the application and the images you want to process The following sections discuss parameters that influence the NI Vision pattern matching algorithm Match Mode You can set the match mode to control how the pattern matching algorithm handles the template at different orientations If you expect the orientation of valid matches to vary less than 5 from the template set CWIMAQMatchPatternOptions MatchMode to cwimaqMatchShiftInvariant Otherwise set the mode element to cwimaqMatchRotationInvariant ays Note Shift invariant matching is faster than rotation invariant matching National Instruments Corporation 5 17 NI Vision for Visual Basic User Manual Chapter 5 Performing Machine Vision Tasks Minimum Contrast Contrast is the difference between the smallest and largest pixel values in a region You can set the minimum contrast to potentially increase the speed of the pattern matching algorithm The pattern matching algorithm ignores all image regions where contrast values fall beneath a set minimum con
61. d for locating objects with good geometric information Figure 5 12 shows examples of template images with good geometric or shape information Figure 5 12 Examples of Objects on which Geometric Matching is Designed to Work Geometric matching is not suited for template images that are predominantly defined by grayscale or texture information Figure 5 13 shows examples of template images that do not have good geometric information The template image in Figure 5 13a is characterized by the grayscale or texture information in the image Figure 5 13b contains too many edges and will dramatically increase the time geometric matching takes to locate the template in an image Phenylketonurics Contains phenylalanine Store at 59 to 77 F in a dry place Avoid heat and humidity Figure 5 13 Examples of Objects for which Geometric Matching is Not Suited NI Vision for Visual Basic User Manual ni com Chapter 5 Performing Machine Vision Tasks Training the Geometric Matching Algorithm After you create a good template image the geometric matching algorithm has to learn the important features of the template Use the NI Vision Template Editor to select the important features in the template Geometric matching uses the curves found in the template image as the basis for the features that are used for matching Use the NI Vision Template Editor to remove noisy or unimportant curves
62. data Acquiring an Image Use the CWIMAQ control to acquire images with a National Instruments image acquisition device You can use NI Vision for Visual Basic to perform one shot and continuous acquisitions You can choose the acquisition type during design time by setting the value of the Acquisition Type combo box to One Shot or Continuous The Acquisition Type combo box is located on the Acquisition property page of the CWIMAQ control You can set the value at run time by setting the CWIMAQ AcquisitionType property to cwimaqAcquisitionOneShot or cwimaqAcquisitionContinuous One Shot Acquisition Use a one shot acquisition to start an acquisition perform the acquisition and stop the acquisition using a single method The number of frames acquired is equal to the number of images in the images collection Use the CWIMAQ AcquireImage method to perform this operation synchronously Use the CWIMAQ Start method to perform this operation asynchronously For information about synchronous and asynchronous acquisitions refer to the NI IMAQ Help If you want to acquire a single field or frame into a buffer set the image count to 1 This operation is also referred to as a snap Use a snap for low speed or single capture applications The following code illustrates a synchronous snap Private Sub Start_Click CWIMAQ1 AcquisitionType cwimagAcquisitionOneShot CWIMAQ1 AcquireImage End Sub NI Vision for Visual Basic User Manual 2 4
63. define an ROI 1 Call CWMachineVision SetupViewerFor lt shapename gt Selection The following lt shapename gt values are available Annulus Line Point Rectangle and RotatedRect This method configures the viewer to display the appropriate tools for the shape you want to select 2 Draw an ROI on the image Resize or reposition the ROI until it defines the area of the image you want to process 3 Use CWMachineVision GetSelected lt shapename gt FromViewer to programmatically retrieve the shape from the viewer You also can use the techniques described in Chapter 3 Making Grayscale and Color Measurements to select an ROI Table 5 1 indicates which ROI selection methods to use with a given CWMachineVision method Table 5 1 ROI Selection Methods to Use with CWMachineVision Methods CWMachineVision ROI Selection Methods CWMachineVision Method SetupViewerForRotatedRectSelection GetSelectedRotatedRectFromViewer FindPattern MeasureMaximumDistance MeasureMinimumDistance FindStraightEdge LightMeterRectangle SetupViewerForAnnulusSelection FindCircularEdge GetSelectedAnnulusFromViewer FindConcentricEdge SetupViewerForPointSelection LightMeterPoint GetSelectedPointFromViewer SetupViewerForLineSelection LightMeterLine GetSelec tedLineFromViewer NI Vision for Visual Basic User Manual 5 8 ni com Chapter 5 Performing Machine Vision Tasks Defining Regions Programmatically
64. ders or classifies the pixel values surrounding the pixel being processed The pixel being processed is set to the Nth pixel value where N is the order of the filter The number of arrays of pixels that compose the image A gray level or pseudo color image is composed of one plane while an RGB image is composed of three planes one for the red component one for the blue and one for the green A geometric matching technique in which the reference pattern can be partially obscured in the target image Optical Character Recognition The process of analyzing an image to detect and recognize characters text in the image NI Vision for Visual Basic User Manual Glossary OCV offset opening operators optical representation outer gradient P palette particle particle analysis pattern matching picture element pixel pixel aspect ratio pixel calibration NI Vision for Visual Basic User Manual Optical Character Verification A machine vision application that inspects the quality of printed characters The coordinate position in an image where you want to place the origin of another image Setting an offset is useful when performing mask operations An erosion followed by a dilation An opening removes small objects and smooths boundaries of objects in the image Allow masking combination and comparison of images You can use arithmetic and logic operators in NI Vision Contains the low frequenc
65. distortion use the nonlinear method The nonlinear method guarantees accurate results only in the area that the calibration grid covers as shown in Figure 6 6 If the system exhibits both perspective and nonlinear distortion use the nonlinear method to correct for both Set CWIMAQLearnCalibrationOptions CalibrationMethod to cwimaqNonLinearCalibration to chose the nonlinear calibration algorithm 1 Calibration ROI Using the 2 Calibration ROI Using the Perspective Algorithm Nonlinear Algorithm Figure 6 6 Calibration ROIs Using the Learning Score The learning process returns a score that reflects how well the software learned the input image A high learning score indicates that you chose the the appropriate learning algorithm that the grid image complies with the guideline and that the vision system setup is adequate National Instruments Corporation 6 7 NI Vision for Visual Basic User Manual Chapter 6 Calibrating Images B Note A high score does not reflect the accuracy of the system If the learning process returns a learning score below 600 try the following 1 Make sure the grid complies with the guidelines listed in the Defining a Calibration Template section 2 Check the lighting conditions If you have too much or too little lighting the software may estimate the center of the dots incorrectly Also adjust the threshold range to distinguish the dots from the background 3 Se
66. ditional instances of the pattern can produce incorrect results To avoid this reduce the search area so that only the appropriate pattern lies within the search area The time required to locate a pattern in an image depends on both the template size and the search area By reducing the search area or increasing the template size you can reduce the required search time In many inspection applications you have general information about the location of the fiducial Use this information to define a search area For example in a typical component placement application each printed circuit board PCB being tested may not be placed in the same location with the same orientation The location of the PCB in various images can move and rotate within a known range of values as illustrated in Figure 5 11 Figure 5 11a shows the template used to locate the PCB in the image Figure 5 11b shows an image containing a PCB with a fiducial you want to locate Notice the search area around the fiducial If you know before the matching process begins that the PCB can shift or rotate in the image within a fixed range as shown in Figure 5 11c and Figure 5 1 1d respectively you can limit the search for the fiducial to a small region of the image NI Vision for Visual Basic User Manual 5 16 ni com Chapter 5 Performing Machine Vision Tasks Figure 5 11 Selecting a Search Area for Grayscale Pattern Matching Setting Matching Parameters an
67. e 5 10 circles finding 5 9 classifying objects 5 34 color content evaluating in images 3 9 color information learning 3 9 specifying 3 10 color location finding points 5 30 color matching 3 10 color pattern matching finding points 5 24 optimize speed with search strategy 5 28 setting rotation angle ranges 5 30 NI Vision for Visual Basic User Manual Index color pattern matching algorithms training 5 26 using contrast 5 30 color scores 5 29 color sensitivity using to control granularity in template images 5 28 color spectrums learning 3 10 color statistics measuring 3 6 3 7 color template images defining 5 25 color threshold 4 2 color comparing in a specified region 3 11 colors learning 3 12 significant colors in the image 3 10 comparing colors in a specified region 3 11 comparison golden template 5 35 complex images 2 12 converting to arrays 2 12 continuous acquisition 2 5 contrast color pattern matching algorithms 5 30 pattern matching algorithms 5 18 conventions used in the manual ix converting arrays to images 2 6 complex images to arrays 2 12 image to its frequency domain 2 11 pixel coordinates to real world coordinates 5 31 convolution filter 2 10 coordinate systems reference 6 3 coordinate transformation building with edge detection 5 3 building with pattern matching 5 5 correction tables learning 6 8 creating binary images 4 1 4 2 images 2 2 NI Vision applica
68. e a region within the image that contains the grid After you acquire an image of the grid learn the calibration information by inputting the image of the grid into CWIMAQVision LearnCalibrationGrid ay Note If you want to specify a list of points instead of a grid use CWIMAQVision LearnCalibrationPoints to learn the calibration information Use the CWIMAQCalibrationPoints object to specify the pixel to real world mapping National Instruments Corporation 6 5 NI Vision for Visual Basic User Manual Chapter 6 Calibrating Images Specifying Scaling Factors Scaling factors are the real world distances between the dots in the calibration grid in the x and y directions and the units in which the distances are measured Use CWIMAQCalibrationGridOptions GridDescriptor to specify the scaling factors Choosing a Region of Interest Define a learning ROI during the learning process to define a region of the calibration grid you want to learn The software ignores dot centers outside this region when it estimates the transformation Creating a user defined ROI is an effective way to increase correction speeds depending on the other calibration options selected Pass a CWIMAQRegions collection representing the region you want to learn to the Regions parameter of CWIMAQVision LearnCalibrationGrid or CWIMAQVision LearnCalibrationPoints Note The user defined ROI represents the area in which you are interested The learning ROI is separate from
69. e approximately the size of the smallest object you want to separate from the background You also need to specify the local thresholding algorithm to use The local thresholding algorithm options are the Niblack or background correction algorithm Refer to the MI Vision Concepts Manual for more information about local thresholding If you need to threshold a color image use CWIMAQVision ColorThreshold You must specify threshold ranges for each of the color planes Red Green and Blue or Hue Saturation and Luminance The binary image resulting from a color threshold is an 8 bit binary image Improve the Binary Image After you threshold the image you may want to improve the resulting binary image with binary morphology You can use primary binary morphology or advanced binary morphology to remove unwanted particles separate connected particles or improve the shape of particles Primary morphology methods work on the image as a whole by processing pixels individually Advanced morphology operations are built upon the primary morphological operators and work on particles as opposed to pixels NI Vision for Visual Basic User Manual 4 2 ni com Chapter 4 Performing Particle Analysis The advanced morphology methods that improve binary images require that you specify the type of connectivity to use Connectivity specifies how NI Vision determines if two adjacent pixels belong to the same particle If you have a particle that contains
70. e listed at the front of this manual You also can visit the Worldwide Offices section of ni com niglobal to access the branch office Web sites which provide up to date contact information support phone numbers email addresses and current events National Instruments Corporation A 1 NI Vision for Visual Basic User Manual Glossary Numbers 1D 2D 3D AIPD alignment alpha channel area arithmetic operators array auto median function National Instruments Corporation G 1 One dimensional Two dimensional Three dimensional The National Instruments internal image file format used for saving complex images and calibration information associated with an image extension APD The process by which a machine vision application determines the location orientation and scale of a part being inspected The channel used to code extra information such as gamma correction about a color image The alpha channel is stored as the first byte in the four byte representation of an RGB pixel 1 A rectangular portion of an acquisition window or frame that is controlled and defined by software 2 The size of an object in pixels or user defined units The image operations multiply divide add subtract and modulo An ordered indexed set of data elements of the same type A function that uses dual combinations of opening and closing operations to smooth the boundaries of objects NI Vision for Visual B
71. e same imaging functions for LabWindows CVI and other C development environments as well as VIs for LabVIEW Vision Assistant another NI Vision Development Module software product enables you to prototype your application strategy quickly without having to do any programming Additionally NI offers Vision Builder for Automated Inspection configurable machine vision software that you can use to prototype benchmark and deploy applications NI Vision for Visual Basic Organization NI Vision for Visual Basic consists of five ActiveX controls contained in three files cwimaq ocx cwmv ocx and niocr ocx cwimaq ocx cwimaq ocx contains the following three ActiveX controls and a collection of ActiveX objects CWIMAQ CWIMAOQVision and CWIMAQViewer Refer to the ActiveX Objects section for information about the ActiveX objects National Instruments Corporation 1 1 NI Vision for Visual Basic User Manual Chapter 1 Introduction to NI Vision CWIMAQ Control Use this control to configure and perform an acquisition from the image acquisition device The CWIMAQ control has property pages that allow you to modify various parameters to configure the acquisition and gather information about the image acquisition device Most of the functionality available from the property pages during design time is also available through the properties of the CWIMAQ control during run time The control has methods that allow you to perform and co
72. e technical support resources at ni com support include the following Self Help Resources For answers and solutions visit the award winning National Instruments Web site for software drivers and updates a searchable KnowledgeBase product manuals step by step troubleshooting wizards thousands of example programs tutorials application notes instrument drivers and so on Free Technical Support All registered users receive free Basic Service which includes access to hundreds of Application Engineers worldwide in the NI Discussion Forums at ni com forums National Instruments Application Engineers make sure every question receives an answer For information about other technical support options in your area visit ni com services or contact your local office at ni com contact Training and Certification Visit ni com training for self paced training eLearning virtual classrooms interactive CDs and Certification program information You also can register for instructor led hands on courses at locations around the world System Integration If you have time constraints limited in house technical resources or other project challenges National Instruments Alliance Partner members can help To learn more call your local NI office or visit ni com alliance If you searched ni com and could not find the answers you need contact your local office or NI corporate headquarters Phone numbers for our worldwide offices ar
73. ected a good template Otherwise refine the current template or select a better template until both training and testing are successful NI Vision for Visual Basic User Manual 5 18 ni com Chapter 5 Performing Machine Vision Tasks Using a Ranking Method to Verify Results The manner in which you interpret the pattern matching results depends on the application For typical alignment applications such as finding a fiducial on a wafer the most important information is the position and bounding rectangle of the best match Use CWIMAQPatternMatchReportItem Position and CWIMAQPatternMatchReportItem BoundingPoints to get the position and location of a match In inspection applications such as optical character verification OCV the score of the best match is more useful The score of a match returned by the pattern matching method is an indicator of the closeness between the original pattern and the match found in the image A high score indicates a very close match while a low score indicates a poor match The score can be used as a gauge to determine if a printed character is acceptable Use CWIMAQPatternMatchReportItem Score to get a match score Finding Points Using Geometric Matching The geometric matching algorithm in NI Vision locates regions in a grayscale image that match a model or template of a reference pattern Geometric matching is specialized to locate templates that are characterized by distinct geometric or shape i
74. eee eeeeseeeeeeeeseeeseeneeeseseeeeaeenees 6 5 Specifying Scaling Factors cee eeeeseeseeseeseeeseeseeeseceeeesesseeenees 6 6 Choosing a Region of Interest 0 0 eeeeeeceeeeeeseeeseeeeeaeeneees 6 6 Choosing a Learning Algorithm 0 ieee ee ceeeeeeeeeeeeteeeneeeneeeeees 6 6 Using the Learning SCOre adorei er e AEE AER 6 7 Learning the Error Mapu cece esceseeseeeseceeneeeeeeseeseeseeeseenees 6 8 Learning the Correction Table eee csceseeeeeeseceeeeseeseeenees 6 8 Setting the Scaling Mode eee cee eseceeeecseeeseeeeeeseeeeeeseeeees 6 8 Calibration Invalidation eee ec ceeeceeseesceesecneceseeeessecseeeeeeeenees 6 9 Simple Calibration lt vitnian ines lato di A ee eee ee 6 9 Save Calibration Information sorsra teens cdes suave ccevass A E e 6 10 Attach Calibration Information ieee eeeeeseeeececeeseeeseesesseesseeseeeseeseeeaseaeenseseeeaes 6 10 Appendix A Technical Support and Professional Services Glossary Index NI Vision for Visual Basic User Manual viii ni com About This Manual Conventions This document is intended for engineers and scientists who have knowledge of Microsoft Visual Basic and need to create machine vision and image processing applications using Visual Basic objects The manual guides you through tasks beginning with setting up the imaging system to taking measurements Q 3 bold italic monospace The following conventions appear in this manual The s
75. eeeeseceeceeeceeeeeeeeeeneeenees 5 12 Defining and Creating Effective Template Images 5 13 Training the Pattern Matching Algorithm eerste 5 15 Defining a Search Aferesis everien eniti na ais 5 16 Setting Matching Parameters and Tolerances sesseeeeeeeeeereeeee 5 17 Testing the Search Algorithm on Test Images eee 5 18 Using a Ranking Method to Verify Results eee eeeeeees 5 19 Finding Points Using Geometric Matching eee eeeeeesseeeeeseceeetseteees 5 19 Defining and Creating Effective Template Images 5 20 Training the Geometric Matching Algorithm 0 eee 5 21 Setting Matching Parameters and Tolerances ie eeeeeeseeeeees 5 22 Testing the Search Algorithm on Test Images eee 5 23 Finding Points Using Color Pattern Matching 00 0 eeeeeeeseceeeeeesees 5 24 Defining and Creating Effective Color Template Images 5 25 Training the Color Pattern Matching Algorithm 5 26 Defining a Search Area 0 cssceesecesnteseestoeeessevscesenesoneseeseessnessees 5 27 Setting Matching Parameters and Tolerances ce eeeeeeeeeeeees 5 28 Testing the Search Algorithm on Test Images eee 5 30 Finding Points Using Color Location eee eee eseeseeeeceeeesecseeesessesneeeaees 5 30 Convert Pixel Coordinates to Real World Coordinates cece eceeeesesseeeeeseeeeeeaees 5 31 Make Measurements ioi ii5 cecssicseepscagetigaassesdesesescasedbgasveageiasndesnacadsdabcesgstesdeeg Eaa a aia 5 31 Distance Me s rem
76. els between each line Specify the search direction as left to right or right to left for a horizontally oriented rectangle Specify the search direction as top to bottom or bottom to top for a vertically oriented rectangle CWIMAQVision Spoke works on an annular search region scanning the search lines that are drawn from the center of the region to the outer boundary and that fall within the search area Control the number of lines in the region by specifying the angle in degrees between each line Specify the search direction as either going from the center outward or from the outer boundary to the center CWIMAQVision ConcentricRake works on an annular search region The concentric rake is an adaptation of the rake to an annular region Edge detection is performed along search lines that occur in the search region and that are concentric to the outer circular boundary Control the number of concentric search lines that are used for the edge detection by specifying the radial distance between the concentric lines in pixels Specify the direction of the search as either clockwise or counterclockwise Finding Points Using Pattern Matching The pattern matching algorithms in NI Vision measure the similarity between an idealized representation of a feature called a template and the feature that may be present in an image A feature is defined as a specific pattern of pixels in an image Pattern matching returns the location of the center of t
77. emoveAl1 CWIMAQ1 Images Add numberOfImages CWIMAQ1 Start End Sub National Instruments Corporation 2 5 NI Vision for Visual Basic User Manual Chapter 2 Getting Measurement Ready Images Reading a File Private Sub Stop_Click CWIMAQ1 Stop End Sub Use the CWIMAQVision ReadImage method to open and read data from a file stored on the computer into the image reference You can read from image files stored in several standard formats such as BMP TIFF JPEG JPEG2000 PNG and AIPD In all cases the software automatically converts the pixels it reads into the type of image you pass in Use the CWIMAQVision ReadImageAndVisionInfo method to open an image file containing additional information such as calibration information template information for pattern matching or overlay information For more information about pattern matching templates and overlays refer to Chapter 5 Performing Machine Vision Tasks You also can use the CWIMAQVision GetFileInformation method to retrieve image properties image size pixel depth recommended image type and calibration units without actually reading all the image data Converting an Array to an Image Use the CWIMAQImage ArrayToImage method to convert an array to an image You also can use the CWIMAQImage ImageToArray method to convert an image to an array Display an Image Display an image using the CWIMAQViewer control Use CWIMAQViewer Attach to attach the image you
78. eometric matching algorithm 5 21 troubleshooting NI resources A 1 National Instruments Corporation Index U using learning scores 6 7 multiple template images 5 23 ranking to verify pattern matching results 5 19 V viewing color differences in an image using multiple regions 3 11 Vision for Visual Basic organization 1 1 voiding calibrations 6 9 W watershed transform 4 4 Web resources A 1 NI Vision for Visual Basic User Manual
79. epresenting the frequency information of the image National Instruments Corporation 2 11 NI Vision for Visual Basic User Manual Chapter 2 Getting Measurement Ready Images 2 Improve the image in the frequency domain with a lowpass or highpass frequency filter Specify which type of filter to use with CWIMAQVision CxAttenuate or CWIMAQVision CxTruncate Lowpass filters smooth noise details textures and sharp edges in an image Highpass filters emphasize details textures and sharp edges in images but they also emphasize noise Lowpass attenuation The amount of attenuation is directly proportional to the frequency information At low frequencies there is little attenuation As the frequencies increase the attenuation increases This operation preserves all of the zero frequency information Zero frequency information corresponds to the DC component of the image or the average intensity of the image in the spatial domain Highpass attenuation The amount of attenuation is inversely proportional to the frequency information At high frequencies there is little attenuation As the frequencies decrease the attenuation increases The zero frequency component is removed entirely Lowpass truncation Specify a frequency The frequency components above the ideal cutoff frequency are removed and the frequencies below it remain unaltered Highpass truncation Specify a frequency The frequency components above the ideal cutoff
80. er 5 Performing Machine Vision Tasks read the information from the file into an image using the CWIMAQVision ReadImageAndVisioniInfo B Note As with calibration information overlay information is removed from an image when the image size or orientation changes NI Vision for Visual Basic User Manual 5 38 ni com Calibrating Images This chapter describes how to calibrate the imaging system save calibration information and attach calibration information to an image After you set up the imaging system you may want to calibrate the system If the imaging setup is such that the camera axis is perpendicular or nearly perpendicular to the object under inspection and the lens has no distortion use simple calibration With simple calibration you do not need to learn a template Instead you define the distance between pixels in the horizontal and vertical directions using real world units If the camera axis is not perpendicular to the object under inspection or the lens is distorted use perspective and nonlinear distortion calibration to calibrate the system Perspective and Nonlinear Distortion Calibration Perspective errors and lens aberrations cause images to appear distorted This distortion misplaces information in an image but it does not necessarily destroy the information in the image Calibrate the imaging system if you need to compensate for perspective errors or nonlinear lens distortion Follow these general steps
81. es Use the CWIMAQMatchColorPatternOptions ColorScoreWeight property to set the color score weight National Instruments Corporation 5 29 NI Vision for Visual Basic User Manual Chapter 5 Performing Machine Vision Tasks Minimum Contrast Use the minimum contrast to increase the speed of the color pattern matching algorithm The color pattern matching algorithm ignores all image regions where grayscale contrast values fall beneath a set minimum contrast value Use CWIMAQMatchColorPatternMatchingOptions MinimumContrast to set the minimum contrast Refer to the Setting Matching Parameters and Tolerances section of this chapter for more information about minimum contrast Rotation Angle Ranges If you know that the pattern rotation is restricted to a certain range provide this restriction information to the pattern matching algorithm by using the CWIMAQMatchPatternOptions RotationAngleRanges property This information improves the search time because the color pattern matching algorithm looks for the pattern at fewer angles Refer to Chapter 12 Pattern Matching of the NI Vision Concepts Manual for more information about pattern matching Testing the Search Algorithm on Test Images To determine if the selected template or reference pattern is appropriate for the machine vision application test the template on a few test images by using the CWIMAQVision MatchColorPattern method These test images should reflect the images genera
82. fastest way to find templates in an image e Aggressive Uses a large step size a large amount of subsampling and all the color spectrum information from the template e Balanced Uses values in between the aggressive and conservative strategies e Conservative Uses a very small step size the least amount of subsampling and all the color information present in the template The conservative strategy is the most reliable method to look for a template in any image at potentially reduced speed 3 Note Use the conservative strategy if you have multiple targets located very close to each other in the image Decide on the best strategy by experimenting with the different options Use CWIMAQMatchColorPatternOptions SearchStrategy to select a search strategy Color Score Weight When you search for a template using both color and shape information the color and shape scores generated during the match process are combined to generate the final color pattern matching score The color score weight determines the contribution of the color score to the final color pattern matching score If the template color information is superior to its shape information set the weight higher For example if you use a weight of 1000 the algorithm finds each match by using both color and shape information and then ranks the matches based entirely on their color scores If the weight is 0 the matches are ranked based entirely on only their shape scor
83. for storing 8 bit 16 bit and color images with lossless compression PNG images have the file extension PNG An edge detection algorithm that extracts the contours in gray level values using a 3 x 3 filter kernel A finite combination of successive closing and opening operations that you can use to fill small holes and smooth the boundaries of objects A finite combination of successive opening and closing operations that you can use to remove small particles and smooth the boundaries of objects Obtaining various measurements of objects in an image A property of an event or system in which data is processed as it is acquired instead of being accumulated and processed at a later time The number of rows and columns of pixels An image composed of m rows and n columns has a resolution of Inverts the pixel values in an image producing a photometric negative of the image A color encoding scheme using red green and blue RGB color information where each pixel in the color image is encoded using 32 bits 8 bits for red 8 bits for green 8 bits for blue and 8 bits for the alpha value unused A color encoding scheme using red green and blue RGB color information where each pixel in the color image is encoded using 64 bits 16 bits for red 16 bits for green 16 bits for blue and 16 bits for the alpha value unused An edge detection algorithm that extracts the contours in gray level favoring diagonal edges NI Vision
84. ft com Documentation that accompanies Microsoft Visual Studio In addition to this manual the following documentation resources are available to help you create your vision application NI Vision Concepts Manual Describes the basic concepts of image analysis image processing and machine vision This document also contains in depth discussions about imaging functions for advanced users NI Vision for Visual Basic Reference Help Contains reference information about NI Vision for Visual Basic objects and their associated properties methods and events NI OCR Training Interface Help Contains information about how to use the NI OCR Training Interface to train characters save character sets and verify characters by comparing them to a reference character Example programs TIllustrate common applications you can create with Vision for Visual Basic You can find these examples in the Vision Examples MSVB directory You can find examples of how to create specific applications in Microsoft Visual Basic NET in the Vision Examples MSVB NET directory NI Vision Assistant Tutorial Describes the NI Vision Assistant software interface and guides you through creating example image processing and machine vision applications NI Vision Assistant Help Contains descriptions of the NI Vision Assistant features and functions and provides instructions for using them X ni com Other Documentation National Instruments Corpora
85. g Particle Improve a Binary Image Analysis Make Particle Measurements SN Measurements Measure Measure Grayscale Statistics Color Statistics Locate Objects to Inspect Set Search Areas Find Measurement Points Identify Parts Under Inspection Classify Objects Read Convert Pixel Coordinates to Real World Coordinates Make Measurements Display Results Characters Read Barcodes gt A Chapter 5 Performing Machine Vision Tasks Figure 1 2 Inspection Steps for Building a Vision Application NI Vision for Visual Basic User Manual 1 6 ni com Getting Measurement Ready Images This chapter describes how to set up an imaging system acquire and display an image analyze the image and prepare the image for additional processing Set Up Your Imaging System Before you acquire analyze and process images you must set up an imaging system The manner in which you set up the system depends on the imaging environment and the type of analysis and processing you need to do Your imaging system should produce images with high enough quality so that you can extract the information you need from the images Follow the guidelines below to set up an imaging system 1 Determine the type of equipment you need based on the space constraints and the size of the object you need to inspect For more information refer
86. gments Number of horizontal segments in the particle cwimaqMeasurementNumberOfVertSegments Number of vertical segments in the particle cwimaqMeasurementOrientation The angle of the line that passes through the particle Center of Mass about which the particle has the lowest moment of inertia cwimaqMeasurementParticleAndHolesArea Percentage of the particle Area in relation to its Particle amp Holes Area cwimaqMeasurementPerimeter Sum of the perimeters of each hole in the particle cwimaqMeasurementRatioOfEquivalentEllipseA xes Equivalent Ellipse Major Axis divided by Equivalent Ellipse Minor Axis cwimaqMeasurementRatioOfEquivalentRectSides Equivalent Rect Long Side divided by Equivalent Rect Short Side cwimaqMeasurementSumX The sum of all X coordinates in the particle cwimaqMeasurementSumXX The sum of all X coordinates squared in the particle cwimaqMeasurementSumxXXX The sum of all X coordinates cubed in the particle cwimaqMeasurementSumXX Y The sum of all X coordinates squared times Y coordinates in the particle cwimaqMeasurementSumX Y The sum of all X coordinates times Y coordinates in the particle cwimaqMeasurementSumX Y Y The sum of all X coordinates times Y coordinates squared in the particle cwimaqMeasurementSumY The sum of all Y coordinates in the particle cwimaqMeasurementSumY Y NI Vision for Visual Basi
87. gments of the indicator must be activated Use CWIMAQVision ReadLCD to read the digits of an LCD or LED Identify Parts Under Inspection In addition to making measurements after you set regions of inspection you also can identify parts using classification OCR and barcode reading Classifying Samples Use classification to identify an unknown object by comparing a set of its significant features to a set of features that conceptually represent classes of known objects Typical applications involving classification include the following e Sorting Sorts objects of varied shapes For example sorting different mechanical parts on a conveyor belt into different bins e Inspection Inspects objects by assigning each object an identification score and then rejecting objects that do not closely match members of the training set National Instruments Corporation 5 33 NI Vision for Visual Basic User Manual Chapter 5 Performing Machine Vision Tasks Before you classify objects you must create a classifier file with samples of the objects using the NI Classification Training Interface Go to Start All Programs National Instruments Vision Classification Training to launch the NI Classification Training Interface After you have trained samples of the objects you want to classify use the following methods to classify the image under inspection e Use CWIMAQVision ReadClassifierFile to read in the classifier file you crea
88. he template and the template orientation Follow these generalized steps to find features in an image using pattern matching 1 Define a reference or fiducial pattern in the form of a template image 2 Use the reference pattern to train the pattern matching algorithm with CWIMAQVision LearnPattern2 3 Define an image or an area of an image as the search area A small search area reduces the time to find the features NI Vision for Visual Basic User Manual 5 12 ni com Chapter 5 Performing Machine Vision Tasks 4 Set the tolerances and parameters to specify how the algorithm operates at run time using CWIMAQMatchPatternOptions 5 Test the search algorithm on test images using CWIMAQVision MatchPattern2 6 Verify the results using a ranking method Defining and Creating Effective Template Images The selection of a effective template image plays a critical part in obtaining good results Because the template image represents the pattern that you want to find make sure that all the important and unique characteristics of the pattern are well defined in the image Several factors are critical in creating a template image These critical factors include symmetry feature detail positional information and background information Symmetry A rotationally symmetric template is less sensitive to changes in rotation than one that is rotationally asymmetric A rotationally symmetric template provides good positioning information bu
89. hineVision FindConcentricEdge CWMachineVision FindStraightEdge finds edges based on rectangular search areas as shown in Figure 5 5 CWMachineVision FindConcentrickEdge finds edges based on annular search areas National Instruments Corporation 5 9 NI Vision for Visual Basic User Manual Chapter 5 Performing Machine Vision Tasks 1 Search Region 2 Search Lines 3 Detected Edge Points 4 Line Fit to Edge Points Figure 5 5 Finding a Straight Feature If you want to find points along a circular edge and find the circle that best fits the edge as shown in Figure 5 6 use CWMachineVision FindCircularEdge NI Vision for Visual Basic User Manual 5 10 ni com Chapter 5 Performing Machine Vision Tasks 1 Annular Search Region 3 Detected Edge Points 2 Search Lines 4 Circle Fit To Edge Points Figure 5 6 Finding a Circular Feature These methods locate the intersection points between a set of search lines in the search region and the edge of an object Specify the separation between the lines that the methods use to detect edges The methods determine the intersection points based on their contrast width and steepness The software calculates a best fit line with outliers rejected or a best fit circle through the points it found The methods return the coordinates of the edges found Finding Edge Points Along One Search Contour Use CWIMAQVision SimpleEdge and CWIMAQVision
90. his problem by choosing a template with sufficient background information for grayscale pattern matching while specifying the exclusion of the background color during the color location phase Refer to the Training the Pattern Matching Algorithm section of this chapter for more information about how to ignore colors Training the Color Pattern Matching Algorithm After you have created a good template image the color pattern matching algorithm learns the important features of the template Use CWIMAQVision LearnColorPattern to learn the template The learning process depends on the type of matching that you expect to perform By default the color pattern matching algorithm learns only those features from the template that are necessary for shift invariant matching However if you want to match the template at any orientation the learning process must consider the possibility of arbitrary orientations Use the CWIMAQLearnColorPatternOptions LearnMode property to specify which type of learning mode to use Exclude colors in the template that you are not interested in using during the search phase Typically you should ignore colors that either belong to the background of the object or are not unique to the template reducing the potential for incorrect matches during the color location phase You can learn the colors to ignore using CWIMAQVision LearnColor Use the CWIMAQLearnColorPatternOptions IgnoreBlackAndWhite or CWIMAQLearnColorPatternOpt
91. hose corresponding pixels in the mask image are non zero If a mask image pixel is 0 the corresponding source image pixel is not processed or analyzed 3 Note The image mask must be an 8 bit image for all NI Vision methods except CWIMAQVision Quantify which supports both 8 bit and 16 bit image masks If you want to apply a processing or analysis method to the entire image do not supply the optional mask image Using the same image for both the source image and mask image also has the same effect as not using the mask image except in this case the source image must be an 8 bit image Most operations between two images require that the images have the same type and size However arithmetic operations work between two different types of images For example an arithmetic operation between an 8 bit image and 16 bit image results in a 16 bit image National Instruments Corporation 2 3 NI Vision for Visual Basic User Manual Chapter 2 Getting Measurement Ready Images Acquire or Read an Image After you create an image you can acquire an image into the imaging system in one of the following three ways e Acquire an image with a camera through the image acquisition device e Load an image from a file stored on the computer e Convert the data stored in a 2D array to an image Methods that acquire images load images from file or convert data from a 2D array automatically allocate the memory space required to accommodate the image
92. hrough Measurement amp Automation Explorer MAX Open MAX by double clicking the Measurement amp Automation Explorer icon on the desktop For more information refer to the Measurement amp Automation Explorer for NI IMAQ Help and Measurement amp Automation Explorer for NI IMAQadx Help Calibrate Your Imaging System After you set up the imaging system you may want to calibrate the system Calibrate the imaging system to assign real world coordinates to pixel coordinates and compensate for perspective and nonlinear errors inherent in the imaging system Perspective errors occur when the camera axis is not perpendicular to the object under inspection Nonlinear distortion may occur from aberrations in the camera lens Perspective errors and lens aberrations cause images to appear distorted This distortion displaces information in an image but it does not necessarily destroy the information in the image Use simple calibration if you want only to assign real world coordinates to pixel coordinates Use perspective and nonlinear distortion calibration if you need to compensate for perspective errors and nonlinear lens distortion For detailed information about calibration refer to Chapter 6 Calibrating Images Create an Image 3 NI Vision for Visual Basic User Manual The CWIMAQImage object encapsulates all the information required to represent an image Note CWIMAQImage is referred to as an image in the remainder of thi
93. ic Matching of the NI Vision Concepts Manual for more information about geometric matching Testing the Search Algorithm on Test Images To determine if your selected template or reference pattern is appropriate for your machine vision application test the template on a few test images by using CWIMAQVision MatchGeometricPattern These test images should reflect the images generated by your machine vision application during true operating conditions If the geometric matching algorithm locates the reference pattern in all cases you have selected a good template Otherwise refine the current template or select a better template until both training and testing are successful Using Multiple Template Images Complete the following steps if your application requires locating multiple template images in a target image 1 Use the methods described in the Defining and Creating Effective Template Images and Training the Geometric Matching Algorithm sections to create and train each template image 2 Create a CWIMAQMultipleGeomatricTemplate object Use CWIMAQMultipleGeometricTemplate AddTemplate to add to this object the templates you trained in the previous step Call CWIMAQVision LearnMultipleGeometricPatterns to learn the Multiple Geometric Template National Instruments Corporation 5 23 NI Vision for Visual Basic User Manual Chapter 5 Performing Machine Vision Tasks B Note Ifyou wish to perform the learning process off line use
94. icles ssessseeseeseseeseesesestsresreresrestrresresenesrrsesresrsee 4 4 Using Watershed Transform eeesseessseerssrsresrsresrrsrsresrsresreresresesees 4 4 Using Binary Morphology esssesseessseseseerssesrssrsresrersresrsresreresresesees 4 4 Improving Particle Shapes sseni ne n e e e ea R ES 4 4 Make Particle Measurements niine eiiie e E EAE EEEE EE 4 5 Chapter 5 Performing Machine Vision Tasks Locate Objects to Inspectirnicrasie anr i E T A EN Na 5 2 Using Edge Detection to Build a Coordinate Transformation e 5 3 Using Pattern Matching to Build a Coordinate Transformation 00 5 5 Choosing a Method to Build the Coordinate Transformation 5 7 pet Search ATE S a idee ds a Mo eee 5 8 Defining Regions Interactively 0 eee ceeeceeseeeseeseeeseeseeeseceeeesesseeeaeeeees 5 8 Defining Regions Programmatically eeeseeeeeseeeeeeseeeseeseseeeeseenees 5 9 NI Vision for Visual Basic User Manual vi ni com Contents Find Measurement Points 222 s 2sccisstisseccsesesadesstaceveassngscaunds vtbevsosdestgevstudes Aavbeviauedestebiaes 5 9 Finding Features Using Edge Detection eee eeseeseceeceeeeeeeseseeeeseesees 5 9 Finding Lines or Circles eee eeceeseceeeeseeseeeseceseeseesseeseseeeeneenaes 5 9 Finding Edge Points Along One Search Contour eee 5 11 Finding Edge Points Along Multiple Search Contours 0 00 5 12 Finding Points Using Pattern Matching o00 eee
95. ines 5 9 measurement points 5 9 points along the edge of a circle 5 10 points using color pattern matching 5 24 points using geometric matching 5 19 points using pattern matching 5 12 points with color location 5 30 G geometric matching finding points 5 19 match mode 5 22 occlusion ranges 5 23 rotation angle ranges 5 22 scale factor ranges 5 22 National Instruments Corporation l 3 Index setting parameters and tolerances 5 22 testing search algorithm 5 23 training 5 21 geometrical measurements 5 32 golden template comparison 5 35 granularity specifying requirements for learning a color 3 12 using color sensitivity to control 5 28 grayscale features filtering 2 10 grayscale morphology filtering grayscale features 2 10 grayscale statistics measuring 3 6 H help technical support A 1 highpass attenuation 2 12 filter 2 9 image mask 3 6 images acquiring 2 4 attaching calibration information 2 7 6 10 calibrating 2 2 complex 2 12 creating 2 2 displaying 2 6 evaluating color content 3 9 filtering 2 9 2 10 filtering grayscale features 2 10 highlighting details using LUTs 2 9 improving 2 9 measuring 5 31 reading 2 4 signal to noise ratio 2 9 transitions 2 9 NI Vision for Visual Basic User Manual Index imaging systems setting up 2 1 improving binary images 4 2 images 2 9 particle shapes 4 4 increasing speed of the color pattern matching algorithm 5 3
96. intensity maximum intensity area and the percentage of the image that you analyzed You can specify regions in the image with a labeled image mask A labeled image mask is a binary image that has been processed so that each region in the image mask has a unique intensity value Use CWIMAQVision Labe12 to label the image mask Use CWIMAQVision Centroid2 to compute the energy center of the image or of a region within an image Measure Color Statistics Most image processing and analysis methods apply to 8 bit and 16 bit images However you can analyze and process individual components of a color image Using CWIMAQVision ExtractColorPlanes you can break down a color image into various sets of primary components such as RGB Red Green and Blue HSI Hue Saturation and Intensity HSL Hue Saturation and Luminance or HSV Hue Saturation and Value Each component becomes an 8 bit or 16 bit image that you can process like any other grayscale image Use CWIMAQVision ExtractSingleColorPlane to extract a single color plane from an image Use CWIMAQVision ReplaceColorPlanes to reassemble a color image from a set of three 8 bit or 16 bit images where each image becomes one of the three primary components Figures 3 3 and 3 4 illustrate how a color image breaks down into its three components National Instruments Corporation 3 7 NI Vision for Visual Basic User Manual Chapter 3 Making Grayscale and Color Measurements
97. ions IgnoreColorSpectra properties to ignore background colors The training or learning process is time intensive because the algorithm attempts to find optimal features of the template for the particular matching process However you can train the pattern matching algorithm offline and save the template image using CWIMAQVision WriteImageAndVisionInfo NI Vision for Visual Basic User Manual 5 26 ni com Chapter 5 Performing Machine Vision Tasks Defining a Search Area Two equally important factors define the success of a color pattern matching algorithm accuracy and speed You can define a search area to reduce ambiguity in the search process For example if the image has multiple instances of a pattern and only one instance is required for the inspection task the presence of additional instances of the pattern can produce incorrect results To avoid this reduce the search area so that only the appropriate pattern lies within the search area For example in the fuse box inspection example use the location of the fuses to be inspected to define the search area Because the inspected fuse box may not be in the exact location or have the same orientation in the image as the previous one the search area you define should be large enough to accommodate these variations in the position of the box Figure 5 15 shows how search areas can be selected for different objects 1 Search Area for 20 Amp Fuses 2 Search Area for 25
98. ision NthOrder Highpass filters emphasize details such as edges object boundaries or cracks These details represent sharp transitions in intensity value National Instruments Corporation 2 9 NI Vision for Visual Basic User Manual Chapter 2 Getting Measurement Ready Images You can define your own highpass filter with CWIMAQVision Convolute or CWIMAQVision NthOrder or you can use a predefined highpass filter with CWIMAQVision EdgeFilter or CWIMAQVision CannyEdgeFilter CWIMAQVision EdgeFilter allows you to find edges in an image using predefined edge detection kernels such as the Sobel Prewitt and Roberts kernels Convolution Filter CWIMAQVision Convolute allows you to use a predefined set of lowpass and highpass filters Each filter is defined by a kernel of coefficients Use the CWIMAQKernel object to define the filter Use CWIMAQKernel LoadKerne1 to load a predefined kernel into the object If the predefined kernels do not meet your needs use the CWIMAQKernel SetSize method to set the size of the kernel and the CWIMAQKernel Element property to set the data in the kernel Nth Order Filter CWIMAQVision NthOrder allows you to define a lowpass or highpass filter depending on the value of N that you choose One specific Nth order filter the median filter removes speckle noise which appears as small black and white dots For more information about Nth order filters refer to Chapter 5 Image Processing of the NI Visio
99. l Instruments Corporation 4 5 NI Vision for Visual Basic User Manual Chapter 4 Performing Particle Analysis Table 4 1 Measurement Types Continued Measurement Description cwimaqMeasurementBoundingRectLeft X coordinate of the leftmost particle point cwimaqMeasurementBoundingRectRight X coordinate of the rightmost particle point cwimaqMeasurementBoundingRectTop cwimaqMeasurementBoundingRectWidth Y coordinate of highest particle point Distance between the X coordinate of the leftmost particle point and the X coordinate of the rightmost particle point cwimaqMeasurementCenterMassX X coordinate of the point representing the average position of the total particle mass assuming every point in the particle has a constant density cwimaqMeasurementCenterMass Y Y coordinate of the point representing the average position of the total particle mass assuming every point in the particle has a constant density cwimaqMeasurementCompactnessFactor Area divided by the product of Bounding Rect Width and Bounding Rect Height cwimaqMeasurementConvexHullArea Area of the smallest convex polygon containing all points in the particle cwimaqMeasurementConvexHullPerimeter Perimeter of the smallest convex polygon containing all points in the particle cwimaqMeasurementElongationFactor Max Feret Diameter divided by Equivalent Rect Short Side Feret cwimaqMeasurementE
100. lect another learning algorithm When nonlinear lens distortion is present using perspective projection sometimes results in a low learning score Learning the Error Map An error map helps you gauge the quality of the complete system The error map returns an estimated error range to expect when a pixel coordinate is transformed into a real world coordinate The transformation accuracy may be higher than the value the error range indicates Set CWIMAQLearnCalibrationOptions LearnErrorMap to True to learn the error map Learning the Correction Table If the speed of image correction is a critical factor for the application use a correction table The correction table is a lookup table that contains the real world location information of all the pixels in the image The correction table is stored in memory The extra memory requirements for this option are based on the size of the image Use this option when you want to simultaneously correct multiple images in the vision application Set CWIMAQLearnCalibrationOptions LearnCorrectionTable to True to learn the correction table Setting the Scaling Mode Use the scaling mode option to choose the appearance of the corrected image Set CWIMAQLearnCalibrationOptions CorrectionScalingMode to cwimagScaleToFit or cwimaqScaleToPreserveArea For more information about the scaling mode refer to Chapter 3 System Setup and Calibration of the NI Vision Concepts Manual NI Vision for Visual Ba
101. ling mode 6 8 search areas 5 8 setting up measurement systems 2 1 shape scores 5 29 signal to noise ratio 2 9 simple calibration 6 9 software NI resources A 1 NI Vision for Visual Basic User Manual l 6 specifying color information 3 10 granularity to learn a color 3 12 learning algorithm 6 6 region of interest 6 6 scaling factors 6 6 specifying scaling factors learning calibration information 6 6 speed increasing for color pattern matching algorithms 5 30 increasing for pattern matching algorithms 5 18 support technical A 1 symmetric templates 5 13 T template background information 5 15 coarse features 5 14 defining with colors that are unique to the pattern 5 25 strong edges 5 14 Template Editor 5 21 template images creating 5 13 5 20 defining 5 13 5 20 using color sensitivity to control granularity 5 28 templates background information 5 26 calibration 6 2 coarse features 5 25 detail 5 25 positional information 5 25 strong edges 5 25 test images testing search algorithms 5 18 5 30 ni com testing geometric search algorithm 5 23 search algorithms 5 18 5 30 threshold automatic 4 2 color 4 2 local 4 2 multiple ranges 4 2 tolerances setting for pattern matching 5 17 touching particles separating 4 4 training characters 5 34 color pattern matching algorithms 5 26 pattern matching algorithm 5 15 training and certification NI resources A 1 training the g
102. n All rights reserved Important Information Warranty The media on which you receive National Instruments software are warranted not to fail to execute programming instructions due to defects in materials and workmanship for a period of 90 days from date of shipment as evidenced by receipts or other documentation National Instruments will at its option repair or replace software media that do not execute programming instructions if National Instruments receives notice of such defects during the warranty period National Instruments does not warrant that the operation of the software shall be uninterrupted or error free A Return Material Authorization RMA number must be obtained from the factory and clearly marked on the outside of the package before any equipment will be accepted for warranty work National Instruments will pay the shipping costs of returning to the owner parts which are covered by warranty National Instruments believes that the information in this document is accurate The document has been carefully reviewed for technical accuracy In the event that technical or typographical errors exist National Instruments reserves the right to make changes to subsequent editions of this document without prior notice to holders of this edition The reader should consult National Instruments if errors are suspected In no event shall National Instruments be liable for any damages arising out of or related to this document or the info
103. n Concepts Manual Grayscale Morphology Perform grayscale morphology when you want to filter grayscale features of an image Grayscale morphology helps you remove or enhance isolated features such as bright pixels on a dark background Use these transformations on a grayscale image to enhance non distinct features before thresholding the image in preparation for particle analysis Grayscale morphological transformations which include erosions and dilations compare a pixel to those pixels that surround it An erosion keeps the smallest pixel values A dilation keeps the largest pixel values For more information about grayscale morphology transformations refer to Chapter 5 Image Processing of the NI Vision Concepts Manual NI Vision for Visual Basic User Manual 2 10 ni com Chapter 2 Getting Measurement Ready Images Use CWIMAQVision GrayMorphology to perform one of the following seven transformations e Erosion Reduces the brightness of pixels that are surrounded by neighbors with a lower intensity e Dilation Increases the brightness of pixels surrounded by neighbors with a higher intensity A dilation has the opposite effect of an erosion e Opening Removes bright pixels isolated in dark regions and smooths boundaries e Closing Removes dark pixels isolated in bright regions and smooths boundaries e Proper opening Removes bright pixels isolated in dark regions and smooths the inner contours of particles
104. n Template comparison gradient convolution filter gradient filter gray level gray level dilation gray level erosion grayscale image grayscale morphology NI Vision for Visual Basic User Manual G 6 The nonlinear change in the difference between the video signal s brightness level and the voltage level needed to produce that brightness Information extracted from a grayscale template that are used to locate the template in the target image Geometric features in an image range from low level features such as edges or curves detected in the image to higher level features such as the geometric shapes made by the curves in the image A technique used to locate quickly a grayscale template that is characterized by distinct geometric or shape information within a grayscale image A comparison of the pixel intensities of an image under inspection to a golden template A golden template is an image containing an ideal representation of an object under inspection See gradient filter An edge detection algorithm that extracts the contours in gray level values Gradient filters include the Prewitt and Sobel filters The brightness of a pixel in an image Increases the brightness of pixels in an image that are surrounded by other pixels with a higher intensity Reduces the brightness of pixels in an image that are surrounded by other pixels with a lower intensity An image with monochrome information Functions that
105. n color matching by setting the corresponding component in the input color spectrum array to 1 To set a particular color component follow these steps 1 Copy CWIMAQColorInformation ColorSpectrum or create your own array 2 Set the corresponding components of the array Assign this array to CWIMAQColorInformation ColorSpectrum on the CWIMAQColorInformation object you want to use as input during the match phase For example setting the last component in the color spectrum to 1 ignores the color white Setting the second to last component in the color spectrum array to 1 ignores the color black To ignore other color components in color matching determine the index to the color spectrum by locating the corresponding bins in the color wheel where each bin corresponds to a component in the color spectrum array Ignoring certain colors such as the background color results in a more accurate color matching score Ignoring the background color also provides more flexibility when defining the regions of interest in the color matching process Ignoring certain colors such as the white color created by glare on a metallic surface also improves the accuracy of the color matching Experiment learning the color information about different parts of the images to determine which colors to ignore For more information about the color wheel and color bins refer to Chapter 15 Color Inspection of the NI Vision Concepts Manual National In
106. n range for example between 15 and 15 provide this restriction information to the geometric matching algorithm in the CWIMAQMatchGeometricPatternOptions RotationAngleRanges property This information improves your search time because the geometric matching algorithm looks for the pattern at fewer angles Scale Factor Ranges When you enable scale invariant matching geometric matching searches for occurrences of the template in the image regardless of whether valid matches are of a different size The default scale range is 75 to 125 If you know that the scale range of the valid matches is restricted to a certain range for example between 90 and 110 provide this NI Vision for Visual Basic User Manual 5 22 ni com Chapter 5 Performing Machine Vision Tasks restriction information to the geometric matching algorithm in the CWIMAQMatchGeometricPatternOptions ScaleRange property Occlusion Ranges When you enable occlusion invariant matching geometric matching searches for occurrences of the template in the image allowing for a specified percentage of the template to be occluded The default occlusion range is 0 to 25 If you know that the occlusion range of the valid matches is restricted to a certain range for example between 0 and 10 provide this restriction information to the geometric matching algorithm in the CWIMAQMatchGeometricPatternOptions OcclusionRange property Refer to the Chapter 13 Geometr
107. nce information where each image in the pixel is encoded using 32 bits 8 bits for hue 8 bits for saturation 8 bits for luminance and 8 unused bits A color encoding scheme in hue saturation and value Represents the dominant color of a pixel The hue function is a continuous function that covers all the possible colors generated using the R G and B primaries See also RGB Hertz Frequency in units of 1 second NI Vision for Visual Basic User Manual Glossary VO image image border Image Browser image buffer image definition image display environment image enhancement image file image format image mask image palette Input output The transfer of data to from a computer system involving communications channels operator interface devices and or data acquisition and control interfaces A two dimensional light intensity function f x y where x and y denote spatial coordinates and the value f at any point x y is proportional to the brightness at that point A user defined region of pixels surrounding an image Functions that process pixels based on the value of the pixel neighbors require image borders An image that contains thumbnails of images to analyze or process in a vision application A memory location used to store images The number of values a pixel can take on which is the number of colors or shades that you can see in the image A window or control that displays an image
108. nd access the regions using the CWIMAQViewer Regions property The individual CWIMAQRegion objects provide access to the shapes in the collection Each region has one shape object associated with it Use the CWIMAQRegion Shape property to determine what type of shape the National Instruments Corporation 3 5 NI Vision for Visual Basic User Manual Chapter 3 Making Grayscale and Color Measurements CWIMAQRegion contains When you know the type of shape that the region contains you can set the region into a shape variable and use that variable to manipulate the shape properties For example the following code resizes a rectangle selected on the viewer Dim MyRectangle As CWIMAQRectangle Set MyRectangle CWIMAQViewerl Regions 1 MyRectangle Width 100 MyRectangle Height 100 You also can pass CWIMAQRegion objects to any NI Vision method that takes a shape as a parameter However if the CWIMAQRegion does not contain the type of shape object that the method requires a type mismatch error results Defining Regions with Masks You can define regions to process with image masks An image mask is an 8 bit image of the same size as or smaller than the image you want to process Pixels in the mask image determine if the corresponding pixel in the source image needs to be processed If a pixel in the image mask has a value other than 0 the corresponding pixel in the source image is processed If a pixel in the image mask has a value
109. nformation Geometric matching finds template matches regardless of lighting variation blur noise occlusion and geometric transformations such as shifting rotation or scaling of the template Geometric matching returns the location of the center of the template the template orientation and the scale of the template 1 Define a reference or fiducial pattern to use as a template image 2 Use the reference pattern to train the geometric matching algorithm with the NI Vision Template Editor Go to Start All Programs National Instruments Vision Template Editor to launch the NI Vision Template Editor 3 Define an image or an area of an image as the search area A small search area can reduce the time to find the features 4 Set the tolerances and parameters to specify how the algorithm operates at run time using CWIMAQMatchGeometricPatternOptions 5 Test the search algorithm on test images using CWIMAQVision MatchGeometricPattern National Instruments Corporation 5 19 NI Vision for Visual Basic User Manual Chapter 5 Performing Machine Vision Tasks Defining and Creating Effective Template Images The selection of an effective template image plays a critical part in obtaining good results Because the template image represents the pattern that you want to find make sure that all the important and unique characteristics of the pattern are well defined in the image The geometric matching algorithm is optimize
110. ni com Chapter 2 Getting Measurement Ready Images If you want to acquire multiple frames set the image count to the number of frames you want to acquire This operation is called a sequence Use a sequence for applications that process multiple images The following code illustrates an asynchronous sequence where numberof Images is the number of images that you want to process Private Sub Start_Click CWIMAQ1 AcquisitionType cwimaqAcquisitionOneShot CWIMAQ1 Images RemoveAll CWIMAQ1 Images Add numberOfImages CWIMAQ1 Start End Sub Continuous Acquisition Use a continuous acquisition to start an acquisition and continuously acquire frames into the image buffers and then explicitly stop the acquisition Use the CwIMAQ Start method to start the acquisition Use the CWIMAQ Stop method to stop the acquisition If you use a single buffer for the acquisition this operation is called a grab The following code illustrates a grab Private Sub Start_Click CWIMAQ1 AcquisitionType cwimaqAcquisitionContinuous CWIMAQ1 Start End Sub Private Sub Stop_Click CWIMAQ1 Stop End Sub A ring operation uses multiple buffers for the acquisition Use a ring for high speed applications that require processing on every image The following code illustrates a ring where numberof Images is the number of images that you want to process Private Sub Start_Click CWIMAQ1 AcquisitionType cwimaqAcquisitionContinuous CWIMAQ1 Images R
111. nt to calibrate The output image is the inspection image with the calibration information attached to it Using the calibration information attached to the image you can accurately convert pixel coordinates to real world coordinates to make any of the analytic geometry measurements with NI Vision for Visual Basic User Manual 6 10 ni com Chapter 6 Calibrating Images CWIMAQVision ConvertPixelToRealWorldCoordinates If the application requires shape measurements correct the image by removing distortion with CWIMAQVision CorrectCalibratedImage 3 Note Correcting images is a time intensive operation A calibrated image is different from a corrected image 3 Note Because calibration information is part of the image it is propagated throughout the processing and analysis of the image Methods that modify the image size such as an image rotation method void the calibration information Use CWIMAQVision WriteImageAndVisionInfo to save the image and all of the attached calibration information to a file If you modify the image after using CWIMAQVision WriteImageAndVisionInfo you must relearn the calibration information and use CWIMAQVision WriteImageAndVisionInfo again National Instruments Corporation 6 11 NI Vision for Visual Basic User Manual Technical Support and Professional Services Visit the following sections of the National Instruments Web site at ni com for technical support and professional services Support Onlin
112. ntS niie ean E AR E 5 31 Analytic Geometry Measurements ssesssseseessesersreererestrersresersesrrsesresrerestse 5 32 Instrument Reader Measurements ss sssesssesessseeersresrsrrsrrrrsreerrenrsresrsererestse 5 33 Identify Parts Under Inspection siisii ssis nassir a arere sriet siso tarais ssar iari oia deot 5 33 Classifying Samples niiit uses sees ge a e a iiai 5 33 Reading Ch racterS sssioiinai nnn nani aie st enees 5 34 Reading Barc deSiaenonreo nnna na en i e nia oa 5 34 Read 1D Barcodes iigiin oa n n a 5 34 Read Data Matrix Code essesseeseseeesesreresrerrsrrsreresrestesesrnsesresrerestee 5 35 Read OR Codere eane TAS E 5 35 Read PDF417 Codering ienaa o E a E E deh 5 35 Inspect Image for Defects ss iiris rri tses aperire brroriai eaer arra E EEr EATS Eaa ESEE T Eia 5 35 Compare to Golden Template 0 0 0 cece eseeseceeceseeseeesecaeeeeeeeenseseeeaees 5 35 Verify Characters scscssoiccesssccassssecuacuiestecagccaee a ria R G teases 5 36 Display Results eisenii nsetti ea aena E a a EAA EEA Eaa 5 36 National Instruments Corporation vii NI Vision for Visual Basic User Manual Contents Chapter 6 Calibrating Images Perspective and Nonlinear Distortion Calibration eee eeeeseeseceeceeeeeeeeeeeeeees 6 1 Defining a Calibration Template cece ceeeeeceeeeeeseeesecseeeseeneenseeeenaes 6 2 Defining a Reference Coordinate System ec eseeceseeseeecescseeseeeeeeees 6 3 Learning Calibration Information 0 0 0
113. ntitative inspection analysis Uses an M shaped structuring element in the skeleton function The technique used to simultaneously locate multiple grayscale templates within a grayscale image NI Vision for Visual Basic User Manual G 12 ni com neighbor neighborhood operations NI IMAQ NI IMAQdx nonlinear filter nonlinear gradient filter nonlinear Prewitt filter nonlinear Sobel filter Nth order filter number of planes in an image 0 occlusion invariant matching OCR National Instruments Corporation G 13 Glossary A pixel whose value affects the value of a nearby pixel when an image is processed The neighbors of a pixel are usually defined by a kernel or a structuring element Operations on a point in an image that take into consideration the values of the pixels neighboring that point The driver software for National Instruments image acquisition hardware The National Instruments driver software for IEEE 1394 and GigE Vision cameras Replaces each pixel value with a nonlinear function of its surrounding pixels A highpass edge extraction filter that favors vertical edges A highpass edge extraction filter based on two dimensional gradient information A highpass edge extraction filter based on two dimensional gradient information The filter has a smoothing effect that reduces noise enhancements caused by gradient operators Filters an image using a nonlinear filter This filter or
114. ntrol acquisitions as well Note You must have the NI IMAQ driver software installed on the target system to use the CWIMAQ control For information about NI IMAQ refer to the NI IMAQ Help that came with the NI image acquisition device CWIMAQVision Control Use this control to analyze and process images and their related data The CWIMAOQVision control provides methods for reading and writing images to and from files analyzing images and performing a variety of image processing algorithms on images CWIMAQViewer Control Use this control to display images and provide the interface through which the user will interact with the displayed image This includes the ability to zoom and pan images and to draw regions of interest ROIs on an image The CWIMAQViewer control has property pages that allow you to configure the viewer s appearance and behavior during design time as well as properties that you can configure during run time The control has methods that allow you to attach images to and detach images from the viewer for display purposes ay Note The CWIMAQViewer control is referred to as a viewer in the remainder of this document niocr ocx niocr ocx provides one ActiveX control and a collection of ActiveX objects you use in a machine vision application to perform optical character recognition OCR NI Vision for Visual Basic User Manual 1 2 ni com Chapter 1 Introduction to NI Vision NIOCR Control Use this control t
115. o attach the calibration information of the current setup to each image you acquire use CWIMAQVision SetCalibrationInformation2 This method takes in a source image that contains the calibration information and a destination image that you want to calibrate The output image is the destination image with the calibration information attached to it For detailed information about calibration refer to Chapter 6 Calibrating Images Note Because calibration information is part of the image it is propagated throughout the processing and analysis of the image Methods that modify the image size such as geometrical transforms void the calibration information Use CWIMAQVision WriteImageAndVisionInfo to save the image and all of the attached calibration information to a file Analyze an Image When you acquire and display an image you may want to analyze the contents of the image for the following reasons e To determine if the image quality is high enough for the inspection task e To obtain the values of parameters that you want to use in processing methods during the inspection process The histogram and line profile tools can help you analyze the quality of the images National Instruments Corporation 2 7 NI Vision for Visual Basic User Manual Chapter 2 Getting Measurement Ready Images Use CWIMAQVision Histogram2 to analyze the overall grayscale distribution in the image Use the histogram of the image to analyze two imp
116. o perform OCR which is the process by which the machine vision software reads text and or characters in an image OCR consists of the following two procedures e Training characters e Reading characters Training characters is the process by which you teach the machine vision software the types of characters and or patterns you want to read in the image during the reading procedure You can use NI Vision to train any number of characters creating a character set which is the set of characters that you later compare with objects during the reading procedure You store the character set you create in a character set file Training might be a one time process or it might be a process you repeat several times creating several character sets to broaden the scope of characters you want to detect in an image Reading characters is the process by which the machine vision application you create analyzes an image to determine if the objects match the characters you trained The machine vision application reads characters in an image using the character set that you created when you trained characters cwmV 0CX cwmv ocx contains one ActiveX control and a collection of ActiveX objects Refer to the ActiveX Objects section for more information about ActiveX objects CWMachineVision Control Use this control to perform high level machine vision tasks such as measuring distances This control is written entirely in Visual Basic using the meth
117. ods on the CWIMAQVision and CWIMAQViewer controls The source code for the CWMachineVision control is included in the product For more information about CWMachineVision methods refer to Chapter 5 Performing Machine Vision Tasks Tip Refer to the source code of the CWMachineVision control for an example of how to use the CWIMAQVision methods National Instruments Corporation 1 3 NI Vision for Visual Basic User Manual Chapter 1 Introduction to NI Vision ActiveX Objects Use the objects to group related input parameters and output parameters to certain methods thus reducing the number of parameters that you actually need to pass to those methods 3 Note ActiveX objects in cwimaq ocx have a CWIMAQ prefix objects in niocr ocx have an NIOCR prefix and objects in cwmv ocx have a CWMV prefix You must create an ActiveX object before you can use it You can use the New keyword in Visual Basic to create these objects For example use the following syntax to create and store an image in a variable named image Dim image As New CWIMAQImage Tip Ifyou intend to develop an application in Visual C National Instruments recommends that you use NI Vision for LabWindows CVI However if you decide to use NI Vision for Visual Basic to develop applications for Visual C you can create objects using the respective Create methods on the CWIMAQ Vision control or CWMachineVision control For example to create a CWIMAQImage object use the
118. oftware defines a default coordinate system as follows 1 The origin is placed at the point in the list with the lowest x coordinate value and then the lowest y coordinate value 2 The angle is set to 0 The axis direction is set to indirect using CWIMAQCoordinateSystem AxisOrientation _ cwimaqAxisOrientationIndirect If you define a coordinate system yourself carefully consider the requirements of the application e Express the origin in pixels Always choose an origin location that lies within the calibration grid so that you can convert the location to real world units e Specify the angle as the angle between the x axis of the new coordinate system x and the top row of dots x as shown in Figure 6 4 If the imaging system exhibits nonlinear distortion you cannot visualize the angle as you can in Figure 6 4 because the dots do not appear in straight lines NI Vision for Visual Basic User Manual 6 4 ni com Chapter 6 Calibrating Images h y 1 Default Origin in a Calibration Grid Image 2 User Defined Origin Figure 6 4 Defining a Coordinate System Learning Calibration Information After you define a calibration grid and reference axis acquire an image of the grid using the current imaging setup For information about acquiring images refer to the Acquire or Read an Image section of Chapter 2 Getting Measurement Ready Images The grid does not need to occupy the entire image You can choos
119. or applied to the luma and chroma components of a color pixel in the color decoding process The smallest convex polygon that can encapsulate a particle Computes the convex hull of objects in a binary image See linear filter 2D matrices or templates used to represent the filter in the filtering process The contents of these kernels are a discrete two dimensional representation of the impulse response of the filter that they represent The process of finding curves or connected edge points in a grayscale image Curves usually represent the boundaries of objects in the image Similar to the distance functions but with more accurate results A characteristic of a system that describes how consistently it can respond to external events or perform operations within a given time limit An image f x y that has been converted into a discrete number of pixels Both spatial coordinates and brightness are specified Increases the size of an object along its boundary and removes tiny holes in the object Software that controls a specific hardware device such as an NI image acquisition or DAQ device NI Vision for Visual Basic User Manual G 4 ni com E edge edge contrast edge detection edge steepness energy center equalize function erosion exponential and gamma corrections exponential function FFT fiducial Fourier transform frequency filters function National Instruments Corporation G 5 Glo
120. ortant criteria that define the quality of an image saturation and contrast If the image does not have enough light the majority of the pixels will have low intensity values which appear as a concentration of peaks on the left side of the histogram If the image has too much light the majority of the pixels will have a high intensity values which appear as a concentration of peaks on the right side of the histogram If the image has an appropriate amount of contrast the histogram will have distinct regions of pixel concentrations Use the histogram information to decide if the image quality is high enough to separate objects of interest from the background If the image quality meets your needs use the histogram to determine the range of pixel values that correspond to objects in the image You can use this range in processing methods such as determining a threshold range during particle analysis If the image quality does not meet your needs try to improve the imaging conditions to get the appropriate image quality You may need to re evaluate and modify each component of the imaging setup lighting equipment and setup lens tuning camera operation mode and acquisition board parameters If you reach the best possible conditions with the setup but the image quality still does not meet your needs try to improve the image quality using the image processing techniques described in the Improve an Image section of this chapter Use CWIMAQ
121. perform morphological operations on a gray level image ni com H highpass attenuation highpass filter highpass frequency filter highpass truncation histogram histogram equalization histogram inversion histograph hit miss function HSI HSL HSV hue National Instruments Corporation G 7 Glossary The inverse of lowpass attenuation Emphasizes the intensity variations in an image detects edges or object boundaries and enhances fine details in an image Removes or attenuates low frequencies present in the frequency domain of the image A highpass frequency filter suppresses information related to slow variations of light intensities in the spatial image The inverse of lowpass truncation Indicates the quantitative distribution of the pixels of an image per gray level value Transforms the gray level values of the pixels of an image to occupy the entire range of the histogram thus increasing the contrast of the image The histogram range in an 8 bit image is 0 to 255 Finds the photometric negative of an image The histogram of a reversed image is equal to the original histogram flipped horizontally around the center of the histogram Histogram that can be wired directly into a graph Locates objects in the image similar to the pattern defined in the structuring element A color encoding scheme in hue saturation and intensity A color encoding scheme using hue saturation and lumina
122. ples NI resources A 1 R ranking verifying pattern matching results 5 19 reading barcodes 5 34 characters 5 34 files 2 6 AIPD 2 6 BMP 2 6 JPEG 2 6 JPEG2000 2 6 PNG 2 6 TIFF 2 6 images 2 4 reference coordinate systems 6 3 defining 6 3 region of interest specifying 6 6 regions defining interactively 5 8 programmatically defining 5 9 regions of interest defining 3 1 defining interactively 3 1 defining with masks 3 6 related documentation x removing unwanted particles 4 3 NI Vision for Visual Basic User Manual Index results displaying 5 36 verifying for pattern matching 5 19 ROI selection methods 5 8 ROIs adding shapes 3 5 programmatically defining 3 5 rotation angle ranges setting for color pattern matching 5 30 setting for pattern matching 5 18 rotationally symmetric template 5 25 S saving calibration information 6 10 scaling factors specifying 6 6 scaling mode setting 6 8 search algorithms testing on test images 5 18 5 30 search area defining 5 16 5 27 search areas setting 5 8 search contour finding points 5 11 search contours finding edge points along multiple search contours 5 12 search strategy using to optimize the speed of color pattern matching algorithms 5 28 selecting significant colors for the object 3 10 separating touching particles 4 4 setting pattern matching tolerances 5 17 5 28 rotation angle ranges for pattern matching 5 18 5 30 sca
123. quivalentEllipseMajorAxis Length of the major axis of the ellipse with the same perimeter and area as the particle cwimaqMeasurementEquivalentEllipseMinorA xis Length of the minor axis of the ellipse with the same perimeter and area as the particle NI Vision for Visual Basic User Manual 4 6 ni com Chapter 4 Performing Particle Analysis Table 4 1 Measurement Types Continued Measurement Description cwimaqMeasurementEquivalentEllipseMinorA xisFeret Length of the minor axis of the ellipse with the same area as the particle and Major Axis equal in length to the Max Feret Diameter cwimaqMeasurementEquivalentRectDiagonal Distance between opposite corners of the rectangle with the same perimeter and area as the particle cwimaqMeasurementEquivalentRectLongSide Longest side of the rectangle with the same perimeter and area as the particle cwimaqMeasurementEquivalentRectShortSide Shortest side of the rectangle with the same perimeter and area as the particle cwimaqMeasurementEquivalentRectShortSideFeret Shortest side of the rectangle with the same area as the particle and longest side equal in length to the Max Feret Diameter cwimaqMeasurementFirstPixelX X coordinate of the highest leftmost particle pixel cwimaqMeasurementFirstPixel Y Y coordinate of the highest leftmost particle pixel cwimaqMeasurementHeywoodCircularity Factor Perimete
124. r divided by the circumference of a circle with the same area cwimaqMeasurementHolesArea Sum of the areas of each hole in the particle cwimaqMeasurementHolesPerimeter Sum of the perimeters of each hole in the particle cwimaqMeasurementHuMoment1 The first Hu moment cwimaqMeasurementHuMoment2 The second Hu moment cwimaqMeasurementHuMoment3 The third Hu moment cwimaqMeasurementHuMoment4 The fourth Hu moment cwimaqMeasurementHuMoment5 The fifth Hu moment cwimaqMeasurementHuMoment6 The sixth Hu moment National Instruments Corporation 4 7 NI Vision for Visual Basic User Manual Chapter 4 Performing Particle Analysis Table 4 1 Measurement Types Continued Measurement Description cwimaqMeasurementHuMoment7 The seventh Hu moment cwimaqMeasurementHydraulicRadius The particle area divided by the particle perimeter cwimaqMeasurementImageArea cwimaqMeasurementMaxFeretDiameter Area of the image Distance between the start and end of the line segment connecting the two perimeter points that are the furthest apart cwimaqMeasurementMaxFeretDiameterEndX X coordinate of the end of the line segment connecting the two perimeter points that are the furthest apart cwimaqMeasurementMaxFeretDiameterEndY Y coordinate of the end of the line segment connecting the two perimeter points that are the furthest apart
125. r methods that directly or indirectly apply lookup tables to images e CWIMAQVision MathLookup Converts the pixel values of an image by replacing them with values from a predefined lookup table NI Vision has seven predefined lookup tables based on mathematical transformations For more information about these lookup tables refer to Chapter 5 Image Processing in the NI Vision Concepts Manual CWIMAQVision UserLookup Converts the pixel values of an image by replacing them with values from a user defined lookup table e CWIMAQVision Equalize2 Distributes the grayscale values evenly within a given grayscale range Use this method to increase the contrast in images containing few grayscale values e CWIMAQVision Inverse Inverts the pixel intensities of an image to compute the negative of the image For example use this method before applying an automatic threshold to the image if the background pixels are brighter than the object pixels Filters Filter the image when you need to improve the sharpness of transitions in the image or increase the overall signal to noise ratio of the image You can choose either a lowpass or highpass filter depending on your needs Lowpass filters remove insignificant details by smoothing the image removing sharp details and smoothing the edges between the objects and the background You can use CWIMAQVision LowPass or define your own lowpass filter with CWIMAQVision Convolute or CWIMAQV
126. rmation contained in it EXCEPT AS SPECIFIED HEREIN NATIONAL INSTRUMENTS MAKES NO WARRANTIES EXPRESS OR IMPLIED AND SPECIFICALLY DISCLAIMS ANY WARRANTY OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE CUSTOMER S RIGHT TO RECOVER DAMAGES CAUSED BY FAULT OR NEGLIGENCE ON THE PART OF NATIONAL INSTRUMENTS SHALL BE LIMITED TO THE AMOUNT THERETOFORE PAID BY THE CUSTOMER NATIONAL INSTRUMENTS WILL NOT BE LIABLE FOR DAMAGES RESULTING FROM LOSS OF DATA PROFITS USE OF PRODUCTS OR INCIDENTAL OR CONSEQUENTIAL DAMAGES EVEN IF ADVISED OF THE POSSIBILITY THEREOF This limitation of the liability of National Instruments will apply regardless of the form of action whether in contract or tort including negligence Any action against National Instruments must be brought within one year after the cause of action accrues National Instruments shall not be liable for any delay in performance due to causes beyond its reasonable control The warranty provided herein does not cover damages defects malfunctions or service failures caused by owner s failure to follow the National Instruments installation operation or maintenance instructions owner s modification of the product owner s abuse misuse or negligent acts and power failure or surges fire flood accident actions of third parties or other events outside reasonable control Copyright Under the copyright laws this publication may not be reproduced or transmitted in any form electronic or me
127. s document An image can be one of many types depending on the data it stores The following image types are valid e 8 bit e 16 bit 2 2 ni com Chapter 2 Getting Measurement Ready Images e Single precision floating point e Complex e 32 bit RGB e 32 bit HSL e 64 bit RGB When you create an image it is an 8 bit image by default You can set the Type property on the image object to change the image type When you create an image no memory is allocated to store the image pixels NI Vision methods automatically allocate the appropriate amount of memory when the image size is modified For example methods that acquire or resample an image alter the image size so they allocate the appropriate memory space for the image pixels Most methods belonging to the NI Vision library require an input of one or more image objects The number of images a method takes depends on the image processing function and the type of image you want to use NI Vision methods that analyze the image but do not modify the image contents require the input of one source image Methods that process the contents of the image require one or more source images and a destination image Exceptions to the preceding statements are methods that take a mask image as input The presence of a MaskImage parameter indicates that the processing or analysis is dependent on the contents of the mask image The only pixels in the source image that are processed are those w
128. s in the learning phase draw a Region around the 3 amp fuse in the upper row hold down lt Ctrl gt and draw another Region around the 3 amp fuse in the lower row The new color spectrum represents 3 amp National Instruments Corporation 3 11 NI Vision for Visual Basic User Manual Chapter 3 Making Grayscale and Color Measurements fuses much better and results in high match scores around 800 for both the fuses You can use an unlimited number of samples to learn the representative color spectrum for a specified template 2 1 Regions Used to Learn Color Information Figure 3 8 Using Multiple Regions to Learn Color Distribution Choosing a Color Representation Sensitivity When you learn a color you need to specify the granularity required to specify the color information An image that contains a few well separated colors in the color space requires a lower granularity to describe the color than an image that contains colors that are close to one another in the color space Use the ColorSensitivity parameter of CWIMAQVision LearnColor to specify the granularity you want to use to represent the colors For more information about color sensitivity refer to the Color Sensitivity section of Chapter 5 Performing Machine Vision Tasks NI Vision for Visual Basic User Manual 3 12 ni com Chapter 3 Making Grayscale and Color Measurements Ignoring Learned Colors You can ignore certain color components i
129. s in the search image In such cases grayscale pattern matching can give inaccurate results If the object has color information that differentiates it from the other objects in the scene color provides the machine vision software with the additional information to locate the object Color pattern matching returns the location of the center of the template and the template orientation Follow these general steps to find features in an image using color pattern matching 1 2 NI Vision for Visual Basic User Manual Define a reference or fiducial pattern in the form of a template image Use the reference pattern to train the color pattern matching algorithm with CWIMAQVision LearnColorPattern 5 24 ni com Chapter 5 Performing Machine Vision Tasks 3 Define an image or an area of an image as the search area A small search area reduces the time to find the features 4 Set CWIMAQMatchColorPatternOptions FeatureMode to cwimaqFeatureAll 5 Set the tolerances and parameters to specify how the algorithm operates at run time using CWIMAQMatchColorPatternOptions 6 Test the search algorithm on test images using CWIMAQVision MatchColorPattern 7 Verify the results using a ranking method Defining and Creating Effective Color Template Images The selection of a effective template image plays a critical part in obtaining accurate results with the color pattern matching algorithm Because the template image represents the color
130. s that are very close to colors in the template image use a higher color sensitivity setting A higher sensitivity setting distinguishes colors with very close hue values Three color sensitivity settings are available in NI Vision low medium and high Use the low setting which is the default if the colors in the template are very different from the colors in the background or other objects that you are not interested in Increase the color sensitivity settings as the color differences decrease Use CWIMAQMat chColorPatternOptions ColorSensitivity to set the color sensitivity For information about color sensitivity refer to Chapter 15 Color Inspection of the NI Vision Concepts Manual Search Strategy Use the search strategy to optimize the speed of the color pattern matching algorithm The search strategy controls the step size sub sampling factor and the percentage of color information used from the template NI Vision for Visual Basic User Manual 5 28 ni com Chapter 5 Performing Machine Vision Tasks Use one of the following four search strategies e Very aggressive Uses the largest step size the most sub sampling and only the dominant color from the template to search for the template Use this strategy when the color in the template is almost uniform the template is well contrasted from the background and there is a good amount of separation between different occurrences of the template in the image This strategy is the
131. scribed by the CWIMAQRegions object on an image Tip You can select the color of overlays by using one of these methods If you do not supply a color to an overlay method the CWIMAQOverlay DefaultColor property is used You can configure the following CWMachineVision methods to overlay different types of information about the inspection image e FindStraightEdge FindCircularEdge e FindConcentricEdge e MeasureMaximumDistance e MeasureMinimumDistance e FindPattern e CountAndMeasureObjects e FindCoordTransformUsingRect e FindCoordTransformUsingTwoRects e FindCoordTransformUsingPattern You can overlay the following information with all the above methods except CWMachineVision FindPattern e The search area input into the method e The search lines used for edge detection e The edges detected along the search lines e The result of the method Each of the above CWMachineVision methods has a settings object input that allows you to select the information you want to overlay Set the boolean property that corresponds to the information you want to overlay to True With CWMachineVision FindPattern you can overlay the search area and the result Use CWwIMAQOverlay Clear to clear any previous overlay information from the image Use CWIMAQVision WriteImageAndVisionInfo to save an image with its overlay information to a file You can National Instruments Corporation 5 37 NI Vision for Visual Basic User Manual Chapt
132. sic User Manual 6 8 ni com Chapter 6 Calibrating Images Calibration Invalidation Any image processing operation that changes the image size or orientation voids the calibration information in a calibrated image Examples of methods that void calibration information include CWIMAQVision Resample2 CWIMAQVision Extract2 CWIMAQVision Unwrap and CWIMAQImage ArrayToImage Simple Calibration When the axis of the camera is perpendicular to the image plane and lens distortion is negligible use simple calibration In simple calibration a pixel coordinate is transformed into a real world coordinate through scaling in the horizontal and vertical directions Use simple calibration to map pixel coordinates to real world coordinates directly without a calibration grid The software rotates and scales a pixel coordinate according to predefined coordinate reference and scaling factors You can assign the calibration to an arbitrary image using CWIMAQVision SetSimpleCalibration To perform a simple calibration set a coordinate system angle center and axis direction and scaling factors on the defined axis as shown in Figure 6 7 Express the angle between the x axis and the horizontal axis of the image in degrees Express the center as the position in pixels where you want the coordinate system origin Set the axis direction to direct or indirect Simple calibration also offers a correction table option and a scaling mode option Use C
133. sion for Visual Basic User Manual Figure 3 2 Tools Information 3 4 ni com Chapter 3 Making Grayscale and Color Measurements During design time use the Menu property page to select which tools appear in the right click menu You also can designate a default tool from this property page During run time set the CWIMAQViewer MenuItems to select the tools to display and set CWIMAQViewer Tool to select the default tool Defining Regions Programmatically You can define ROIs programmatically using the CWIMAQRegions collection In NI Vision shapes are represented by shape objects For example CWIMAQPoint represents a point and CWIMAQLine represents a line Use the following methods listed in Table 3 2 to add various shapes to the regions Table 3 2 Methods that Add Shapes to Regions Method Description AddPoint adds a point to the ROI AddLine adds a line to the ROI AddRectangle adds a rectangle to the ROI AddRotatedRectangle adds a rotated rectangle to the ROI Addoval adds an oval to the ROI AddAnnulus adds an annulus to the ROI AddBrokenLine adds a broken line to the ROI AddPolygon adds a polygon to the ROI AddFreeline adds a free line to the ROI AddFreeregion adds a free region to the ROI AddRegion adds a region object to the ROI Use the CWIMAQRegions CopyTo method to copy all the data from one CWIMAQRegions object to another You can define the regions on a viewer a
134. splay Results You can display the results obtained at various stages of the inspection process on the window that displays the inspection image by overlaying information about an image The software attaches the information that you want to overlay to the image but it does not modify the image Access overlays using the CWIMAQImage Overlays property The CWIMAQOverlays collection contains a single CWIMAQOverlay object that you can access using CWIMAQImage Overlay 1 Note The CwIMAQImage Overlays collection does not support usual collection methods such as Add Remove and RemoveAl1 because they are reserved for future use Use the following methods on the CWIMAQOverlay object to overlay search regions inspection results and other information such as text and pictures Overlays on a viewer image are automatically updated when you call one of these methods e DrawLine Overlays a CWIMAQLine object on an image e DrawConnectedPoints Overlays a CWIMAQPoints collection and draws a line between sequential points e DrawRectangle Overlays a CWIMAQRectangle object on an image NI Vision for Visual Basic User Manual 5 36 ni com Chapter 5 Performing Machine Vision Tasks e DrawOval Overlays a CWIMAQOval object on an image e DrawArc Overlays a CWIMAQArc object on an image e DrawPicture Overlays a picture object onto the image e DrawText Overlays text on an image DrawRegions Overlays an ROI de
135. ssary Defined by a sharp transition in the pixel intensities in an image or along an array of pixels The difference between the average pixel intensity before and the average pixel intensity after the edge Any of several techniques to identify the edges of objects in an image The number of pixels that corresponds to the slope or transition area of an edge The center of mass of a grayscale image See also center of mass See histogram equalization Reduces the size of an object along its boundary and eliminates isolated points in the image Expand the high gray level information in an image while suppressing low gray level information Decreases brightness and increases contrast in bright regions of an image and decreases contrast in dark regions of an image Fast Fourier Transform A method used to compute the Fourier transform of an image A reference pattern on a part that helps a machine vision application find the part s location and orientation in an image Transforms an image from the spatial domain to the frequency domain The counterparts of spatial filters in the frequency domain For images frequency information is in the form of spatial frequency A set of software instructions executed by a single line of code that may have input and or output parameters and returns a value when executed NI Vision for Visual Basic User Manual Glossary G gamma geometric features geometric matching Golde
136. struments Corporation 3 13 NI Vision for Visual Basic User Manual Performing Particle Analysis This chapter describes how to perform particle analysis on the images Use particle analysis to find statistical information about particles such as the presence size number and location of particle regions With this information you can perform many machine vision inspection tasks such as detecting flaws on silicon wafers or detecting soldering defects on electronic boards Examples of how particle analysis can help you perform web inspection tasks include locating structural defects on wood planks or detecting cracks on plastic sheets Figure 4 1 illustrates the steps involved in performing particle analysis Create a Binary Image Improve a Binary Image Make Particle Measurements in Pixels or Real World Units Figure 4 1 Steps for Performing Particle Analysis Create a Binary Image Threshold the grayscale or color image to create a binary image Creating a binary image separates the objects that you want to inspect from the background The threshold operation sets the background pixels to 0 in the binary image while setting the object pixels to a non zero value Object pixels have a value of 1 by default but you can set the object pixels to any value or retain their original value You can use different techniques to threshold the image If all the objects of interest in the grayscale image fall
137. t no orientation information a Rotationally Symmetric b Rotationally Asymmetric Figure 5 7 Symmetry National Instruments Corporation 5 13 NI Vision for Visual Basic User Manual Chapter 5 Performing Machine Vision Tasks Feature Detail A template with relatively coarse features is less sensitive to variations in size and rotation than a model with fine features However the model must contain enough detail to identify it a Good Feature Detail b Ambiguous Feature Detail Figure 5 8 Feature Detail Positional Information A template with strong edges in both the x and y directions is easier to locate a Good Positional Information in x and y b Insufficient Positional Information in y Figure 5 9 Positional Information NI Vision for Visual Basic User Manual 5 14 ni com Chapter 5 Performing Machine Vision Tasks Background Information Unique background information in a template improves search performance and accuracy a Pattern with Insufficient Background Information b Pattern with Sufficient Background Information Figure 5 10 Background Information Training the Pattern Matching Algorithm After you create a good template image the pattern matching algorithm has to learn the important features of the template Use CWIMAQVision LearnPatternz2 to learn the template The learning process depends on the type of matching th
138. ted by the machine vision application during true operating conditions If the color pattern matching algorithm locates the reference pattern in all cases you have selected a good template Otherwise refine the current template or select a better template until both training and testing are successful Finding Points Using Color Location Color location algorithms provide a quick way to locate regions in an image with specific colors Use color location under the following circumstances e Requires the location and the number of regions in an image with their specific color information e Relies on the cumulative color information in the region instead of the color arrangement in the region e Does not require the orientation of the region NI Vision for Visual Basic User Manual 5 30 ni com Chapter 5 Performing Machine Vision Tasks e Does not always require the location with sub pixel accuracy e Does not require shape information for the region Complete the following steps to find features in an image using color location 1 Define a reference pattern in the form of a template image 2 Use the reference pattern to train the color location algorithm with CWIMAQVision LearnColorPattern 3 Define an image or an area of an image as the search area A small search area reduces the time to find the features 4 Set CWIMAQMatchColorPatternOptions FeatureMode to cwimaqFeatureColorInformation 5 Set the tolerances
139. ted with the NI Classification Training Interface e Use CWIMAQClassifier Classify to classify the image under inspection Reading Characters Use OCR to read text and or characters in an image Typical uses for OCR in an inspection application include identifying or classifying components Before you read text and or characters in an image you must create a character set file with samples of the characters using the OCR Training Interface Go to Start All Programs National Instruments Vision OCR Training to launch the OCR Training Interface After you have trained samples of the characters you want to read use the following methods to read the characters e Use NIOCR ReadOCRFile to read in a character set file that you created using the OCR Training Interface e Use NIOCR ReadText to read the characters inside the ROI of the image under inspection Reading Barcodes Use barcode reading objects to read values encoded into 1D barcodes Data Matrix codes and PDF417 codes Read 1D Barcodes To read a 1D barcode locate the barcode in the image using one of the techniques described in the Instrument Reader Measurements section and then pass the Regions parameter into CWIMAQVision ReadBarcode Use CWIMAQVision ReadBarcode to read values encoded in the 1D barcode Specify the type of 1D barcode in the application using the BarcodeType parameter NI Vision supports the following 1D barcode NI Vision for Visual Basic User Man
140. the calibration ROI that is generated by the calibration algorithm Refer to Figure 6 6 for an illustration of calibration ROIs Choosing a Learning Algorithm Select a method in which to learn the calibration information perspective projection or nonlinear Figure 6 5 illustrates the types of errors the image can exhibit Figure 6 5a shows an image of a calibration grid with no errors Figure 6 5b shows an image of a calibration grid with perspective projection Figure 6 5c shows an image of a calibration grid with nonlinear distortion TEKEK 0 ee se as PE PObeag EKEK eoccooooo eo e ee e e eoe ooo o TEREE H e e e e0e00 e ENEEK b O M O O 00o o EEES eaee oe a b c Figure 6 5 Types of Image Distortion NI Vision for Visual Basic User Manual 6 6 ni com Chapter 6 Calibrating Images Choose the perspective projection algorithm when the system exhibits perspective errors only A perspective projection calibration has an accurate transformation even in areas not covered by the calibration grid as shown in Figure 6 6 Set CWIMAQLearnCalibrationOptions CalibrationMethod to cwimagPerspectiveCalibration to choose the perspective calibration algorithm Learning and applying perspective projection is less computationally intensive than the nonlinear method However perspective projection cannot handle nonlinear distortions If the imaging setup exhibits nonlinear
141. tion About This Manual National Instruments image acquisition device user manuals Contain installation instructions and device specific information NI IMAQ Help Contains fundamental programming concepts for NI IMAQ driver software and terminology for using NI image acquisition devices NI IMAQdx Help Contains fundamental programming concepts for NI IMAQdx driver software and terminology for using IEEE 1394 and Gigabit Ethernet GigE cameras NI Developer Zone NIDZ Contains example programs tutorials technical presentations the Instrument Driver Network a measurement glossary an online magazine a product advisor and a community area where you can share ideas questions and source code with developers around the world The NI Developer Zone is located on the National Instruments Web site at ni com zone xi NI Vision for Visual Basic User Manual Introduction to NI Vision This chapter describes the NI Vision for Visual Basic software outlines the NI Vision for Visual Basic architecture and lists the steps for creating a machine vision application 3 Note Refer to the NMI Vision Development Module Readme for information about the system requirements and installation procedures for NI Vision for Visual Basic About NI NI Vision for Visual Basic is a collection of ActiveX controls you can use to develop machine vision and scientific imaging applications The NI Vision Development Module also includes th
142. tions 1 4 template images 5 13 5 20 NI Vision for Visual Basic User Manual l 2 CWIMAQ control 1 2 cwimaq ocx l 1 CWIMAQViewer control 1 2 CWIMAOQVision 1 2 CWMachine Vision control 1 3 CWMachineVision methods 5 8 cwmv ocx 1 3 D data matrix barcodes reading 5 35 defining calibration templates 6 2 color template images 5 25 effective template images 5 13 5 20 reference coordinate systems 6 3 regions interactively 5 8 regions of interest 3 1 regions of interest interactively 3 1 regions programmatically 5 9 ROIs programmatically 3 5 ROIs with masks 3 6 search area 5 27 search areas 5 16 templates with colors that are unique to the pattern 5 25 detecting objects 5 2 diagnostic tools NI resources A 1 displaying images 2 6 results 5 36 distance measurements 5 31 documentation conventions used in manual ix NI resources A 1 related documentation x drivers NI resources A 1 ni com E edge detection 5 3 finding features 5 9 edge points finding along multiple search contours 5 12 error map learning 6 8 evaluating color content of an image 3 9 examples NI resources A 1 F features finding with edge detection 5 9 FFT 2 11 files reading 2 6 filtering grayscale features 2 10 images 2 9 2 10 filters FFT 2 11 finding circles 5 9 edge points along multiple search contours 5 12 edge points along one search contour 5 11 features with edge detection 5 9 l
143. to Chapter 3 System Setup and Calibration of the NI Vision Concepts Manual a Make sure the camera sensor is large enough to satisfy the minimum resolution requirement b Make sure the lens has a depth of field high enough to keep all of the objects in focus regardless of their distance from the lens Also make sure the lens has a focal length that meets your needs c Make sure the lighting provides enough contrast between the object under inspection and the background for you to extract the information you need from the image 2 Position the camera so that it is parallel to the object under inspection If the camera acquires images of the object from an angle perspective errors occur Even though you can compensate for these errors with software NI recommends that you use a perpendicular inspection angle to obtain the fastest and most accurate results 3 Select an image acquisition device that meets your needs National Instruments offers several image acquisition devices such as analog National Instruments Corporation 2 1 NI Vision for Visual Basic User Manual Chapter 2 Getting Measurement Ready Images 9 color and monochrome devices as well as digital devices Visit ni com vision for more information about NI image acquisition devices 4 Configure the driver software for the image acquisition device If you have a National Instruments image acquisition device configure the NI IMAQ or NI IMAQdx driver software t
144. to calibrate the imaging system 1 Define a calibration template 2 Define a reference coordinate system 3 Learn the calibration information After you calibrate the imaging setup you can attach the calibration information to an image Refer to the Attach Calibration Information section of this chapter for more information Depending on your needs you can either apply calibration information in one of the following ways e Convert pixel coordinates to real world coordinates without correcting the image e Create a distortion free image by correcting the image for perspective errors and lens aberrations National Instruments Corporation 6 1 NI Vision for Visual Basic User Manual Chapter 6 Calibrating Images Refer to Chapter 5 Performing Machine Vision Tasks for more information about applying calibration information before making measurements Defining a Calibration Template You can define a calibration template by supplying an image of a grid or providing a list of pixel coordinates and their corresponding real world coordinates This section discusses the grid method in detail A calibration template is a user defined grid of circular dots As shown in Figure 6 1 the grid has constant spacings in the x and y directions You can use any grid but follow these guidelines for the best results The displacement in the x and y directions must equal dx dy The dots must cover the appropriate portion of the working area
145. trast value If the search image has high contrast but contains some low contrast regions you can set a high minimum contrast value Using a high minimum contrast value excludes all areas in the image with low contrast significantly reducing the region in which the pattern matching algorithm must search If the search image has low contrast throughout set a low minimum contrast parameter to ensure that the pattern matching algorithm looks for the template in all regions of the image Use CWIMAQMatchPatternOptions MinimumContrast to set the minimum contrast Rotation Angle Ranges If you know that the pattern rotation is restricted to a certain range such as between 15 to 15 provide this restriction information to the pattern matching algorithm in the CWIMAQMatchPatternOptions RotationAngleRanges property This information improves your search time because the pattern matching algorithm looks for the pattern at fewer angles Refer to Chapter 12 Pattern Matching of the NI Vision Concepts Manual for information about pattern matching Testing the Search Algorithm on Test Images To determine if the selected template or reference pattern is appropriate for the machine vision application test the template on a few test images These test images should reflect the images generated by the machine vision application during true operating conditions If the pattern matching algorithm locates the reference pattern in all cases you have sel
146. ts Manual If you know enough about the shape features of the particles you want to keep use CWIMAQVision ParticleFilter3 to filter out particles that do not interest you If you do not have enough information about the particles you want to keep at this point in the processing use the particle measurement methods to obtain this information before applying a particle filter Refer to the Make Particle Measurements section for more information about the measurement methods National Instruments Corporation 4 3 NI Vision for Visual Basic User Manual Chapter 4 Performing Particle Analysis Separating Touching Particles 3 Use watershed transform or binary morphology to separate touching particles in images Using Watershed Transform Use CWIMAQVision Danielsson to transform the binary image into a grayscale distance map in which each particle pixel is assigned a gray level value equal to its shortest Euclidean distance from the particle border Apply CWIMAQVision WatershedTrans form to the distance map to find the watershed separation lines Use CWwIMAQVision Mask to superimpose the watershed lines on the original image Refer to the NI Vision Concepts Manual for more information about segmenting images using a watershed transform Using Binary Morphology Use CWIMAQVision Separation or apply an erosion or an open operation with CWIMAQVision Morphology to separate touching objects CWIMAQVision Separation is an advanced oper
147. ual 5 34 ni com Chapter 5 Performing Machine Vision Tasks types Codabar Code 39 Code 93 Code 128 EAN 8 EAN 13 Interleaved 2 of 5 MSI UPCA RSS Limited and Pharmacode Read Data Matrix Code Use CWIMAQVision ReadDataMatrixBarcode2 to read values encoded in a Data Matrix code This method can automatically locate the Data Matrix code in an image This method also can automatically determine the appropriate search options for the application However you can improve the performance of the application by specifying control values specific to the application Read QR Code Use CWIMAQVision ReadQRCode to read values encoded in a QR or micro QR code This method can automatically locate the Data Matrix code in an image This method also can automatically determine the appropriate search options for the application However you can improve the performance of the application by specifying control values specific to the application Read PDF417 Code Use CWIMAQVision ReadPDF417Barcode to read values encoded in a PDF417 code By default CWIMAQVision ReadPDF417Barcode automatically locates one or multiple PDF417 codes in an image Y Tip If you need to read only one code per image set the SearchMode parameter to cwimaqBarcode2DSearchSingleConservative to increase the speed of the method Inspect Image for Defects You can inspect an image for defects by comparing it to a golden template and by verifying characters in the image
148. utomatically determines the direction of the horizontal axis in the real world The vertical axis direction can either be indirect as shown in Figure 6 2a or direct as shown in Figure 6 2b Figure 6 2 Axis Direction in the Image Plane If you do not specify a coordinate system the calibration process defines a default coordinate system If you specify a grid for the calibration process the software defines the following default coordinate system as shown in Figure 6 3 1 The origin is placed at the center of the left topmost dot in the calibration grid 2 The angle is set to 0 This aligns the x axis with the first row of dots in the grid as shown in Figure 6 3b 3 The axis direction is set to indirect using CWIMAQCoordinateSystem AxisOrientation _ cwimagAxisOrientationIndirect This aligns the y axis to the first column of the dots in the grid as shown in Figure 6 3b National Instruments Corporation 6 3 NI Vision for Visual Basic User Manual Chapter 6 Calibrating Images oo i P i e 0 00 00 0 gt oe po eocoooecc50e e0 fee eeecccce a09 poe d ecooeoc50e eee gee eeeeccce eo ue EKEK H o 9 M Ai y y a b 1 Origin of a Calibration Grid in the Real World 2 Origin of the Same Calibration Grid in an Image Figure 6 3 A Calibration Grid and an Image of the Grid Note If you specify a list of points instead of a grid for the calibration process the s
149. ve the cursor over the side of a rectangle the cursor changes to indicate that you can click and drag the side to resize the rectangle If you want to draw more than one ROI in a window hold down lt Ctrl gt while drawing additional ROIs You also can use CWIMAQViewer MaxContours to set the maximum number of contours the viewer can have in its ROI In the status bar of the viewer you can display tool information about the characteristics of ROIs you draw as shown in Figure 3 2 Check the Show Tool Info check box on the Status Bar property page during design time or set the CWIMAQViewer ShowToolInfo property to True during run time to display tool information You also can show or hide the tool information from the right click menu National Instruments Corporation 3 3 NI Vision for Visual Basic User Manual Chapter 3 Making Grayscale and Color Measurements X 2 Ye 22r 512x512 1 1 8 Bit Image 182 SY LUD oya Ef Sadi TITE oat E bi Menoa OQ P 8 TJR ae e o eT DEROO bto bto by RGB32 RGBU64 HSL Complex D x 74 351 dX 277 L32 4g 8 Y L131 275 dY 144 A 332 52 512x512 1 1 8 Bit Image 82 1 Anchoring Coordinates of a Region of Interest 5 Pixel Intensity 2 Size of the Image 6 Coordinates of the Mouse 3 Zoom Factor 7 Size of an Active Region of Interest 4 Image Type Indicator 8 Bit 16 Bit Float 8 Length and Horizontal Angle of a Line Region NI Vi
150. y information at the center and the high frequency information at the corners of an FFT transformed image Finds the outer boundary of objects The gradation of colors used to display an image on screen usually defined by a CLUT A connected region or grouping of non zero pixels in a binary image A series of processing operations and analysis functions that produce some information about the particles in an image The technique used to locate quickly a grayscale template within a grayscale image An element of a digital image Also called pixel Picture element The smallest division that makes up the video scan line For display on a computer monitor a pixel s optimum dimension is square aspect ratio of 1 1 or the width equal to the height The ratio between the physical horizontal size and the vertical size of the region covered by the pixel An acquired pixel should optimally be square thus the optimal value is 1 0 but typically it falls between 0 95 and 1 05 depending on camera quality Directly calibrates the physical dimensions of a pixel in an image G 14 ni com pixel depth PNG Prewitt filter proper closing proper opening Q quantitative analysis R real time resolution reverse function RGB RGB U64 Roberts filter National Instruments Corporation G 15 Glossary The number of bits used to represent the gray level of a pixel Portable Network Graphic An image file format
151. ymbol leads you through nested menu items and dialog box options to a final action The sequence File Page Setup Options directs you to pull down the File menu select the Page Setup item and select Options from the last dialog box This icon denotes a tip which alerts you to advisory information This icon denotes a note which alerts you to important information Bold text denotes items that you must select or click in the software such as menu items and dialog box options Bold text also denotes parameter names Italic text denotes variables emphasis a cross reference or an introduction to a key concept Italic text also denotes text that is a placeholder for a word or value that you must supply Text in this font denotes text or characters that you should enter from the keyboard sections of code programming examples and syntax examples This font is also used for the proper names of disk drives paths directories programs subprograms subroutines device names functions operations variables filenames and extensions National Instruments Corporation ix NI Vision for Visual Basic User Manual About This Manual Related Documentation NI Vision NI Vision Assistant NI Vision for Visual Basic User Manual This manual assumes that you are familiar with Visual Basic and can use ActiveX controls in Visual Basic The following are good sources of information about Visual Basic and ActiveX controls msdn microso

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