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National Instruments IMAQ Vision for Measurement Studio Match Mode, Minimum Contrast, Rotation Angle Ranges

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Chapter5 MachineVision

©NationalInstruments Corporation 5-17 IMAQVision for LabWindows/CVI User Manual

However, there may be applications in which the assumptions used in the

algorithmare not optimal. Knowing your particular application and the

imagesyou want to process is useful in selecting the pattern matching

parameters. The following are parameters the influence the IMAQ Vision

pattern matching algorithm.

Match Mode

Youcan set the match mode to control how t he pattern matching algorithm

treatsthe template at different orientations. If you expect the orientation of

validmatches to vary less than ±5°from the template, set the mode element

ofthe options parameter to IMAQ_MATCH_SHIFT_INVARIANT.Otherwise,

setthe mode element to IMAQ_MATCH_ROTATION_INVARIANT.

Shift-invariantm atching is faster than rotation-invariant matching.

Minimum Contrast

You can set the minimum contrast to potentially increase the pattern

matchingalgorithm’s speed. Contrast is the difference between the smallest

andlargest pixel values in a region. The pattern matching algorithm ignores

allimage regions where contrast values fall beneath a set minimum contrast

value.If the search image has high contrast but contains some low contrast

regions, you can set a high minimum contrast value. By using a high

minimum contrast value, you exclude all areas in the image with low

contrast, significantly reducing the region in which the pattern matching

algorithmmust search. If the search image has low contrast throughout, set

al ow minimum contrast parameter to ensure that the pattern matching

algorithm looks for the template in all regions of the image. Use the

minContrastelement of the imaqMatchPattern() options parameter

to set the minimum contrast.

Rotation Angle Ranges

Formatching objects that may rotate in the image, the pattern matching

algorithm,by default, allows any orientation between 0°to 360°.Ifthe

pattern rotation in your application is restrictedt o acertain range (for

example,from –15°to 15°), you can provide this information to the pattern

matching algorithm by setting the angleRanges element of the

imaqMatchPattern()options parame ter. This information improves

your search time because the pattern matching algorithm looks for the

pattern at fewer angles.

Contents

Main IMAQ Page Important Information Warranty Copyright Trademarks WARNINGREGARDING USE OF NATIONAL INSTRUMENTS PRODUCTS Conventions Contents Chapter 1 Introduction to IMAQ Vision Chapter 2 Getting Measurement-Ready Images Chapter 3 Grayscale and Color Measurements Chapter 4 Blob Analysis Chapter 5 Machine Vision Page Introduction to IMAQ Vision About IMAQ Vision Documentation and Examples Application Development Environments IMAQ Vision Function Tree Page IMAQ Machine Vision Function Tree Creating IMAQ Vision Applications Chapter1 Introductionto IMAQ Vision Figure1-2. InspectionSteps for Building a Vision Application IMAQVision for LabWindows/CVI User Manual 1-6 ni.com Getting Measurement-Ready Images Set Up Your Imaging System Calibrate Your Imaging System Create an Image Page Source and Destination Images AcquireorReadanImage Acquiring an Image Reading a File Converting an Array to an Image Display an Image Attach Calibration Information Analyze an Image Improve an Image Lookup Tables Filters Convolution Filter Nth Order Filter Grayscale Morphology FFT Page Complex Image Operations Grayscale and Color Measurements Define Regions of Interest Interactively Defining Regions Page Page Page Page Programmatically Defining Regions Defining Regions with Masks Measure Grayscale Statistics Measure Color Statistics Figure3-4. Primary Components of a Color Image Comparing Colors Learning Color Information Choosing the Right Color Information Specifying the Color Information to Learn Using the Entire Image Using a Region in the Image Using Multiple Regions in the Image Choosing a Color Representation Sensitivity Ignoring Learned Colors Blob Analysis Correct Image Distortion Create a Binary Image Improve the Binary Image Removing Unwanted Blobs Separating TouchingBlobs Improving Blob Shapes Make Particle Measurements Page Page Convert Pixel Coordinates to Real-World Coordinates Machine Vision Locate Objects to Inspect UsingEdgeDetectiontoBuildaCoordinateTransform Page Using Pattern Matching to Build a Coordinate Transform Page Chapter5 MachineVision NationalInstruments Corporation 5-7 IMAQVision for LabWindows/CVI User Manual ChoosingaMethodtoBuildtheCoordinateTransform Figure5-4. Buildinga Coordinate Transform Set Search Areas Interactively Defining Regions Programmatically Defining Regions Find Measurement Points Finding Features Using Edge Detection Finding Lines or Circles Figure5-5. Findinga Straight Feature Finding Edge Points Along One Search Contour Finding Edge Points Along Multiple Search Contours Finding Points Using Pattern Matching Defining and Create Good TemplateImages Symmetry Feature detail Positional information Background information Trainingthe Pattern Matching Algorithm Defining a Search Area Setting Matching Parameters and Tolerances Match Mode Minimum Contrast Rotation Angle Ranges Testingthe Search Algorithm on Test Images Using a Ranking Method to Verify Results Finding Points Using Color Pattern Matching Defining and Creating Good Color TemplateImages Color Information Symmetry Feature Detail Positional Information Trainingthe Color Pattern Matching Algorithm Defining a Search Area Setting Matching Parameters and Tolerances Color Sensitivity Search Strategy Color Score Weight Minimum Contrast Rotation Angle Ranges Testingthe Search Algorithm on Test Images Finding Points Using Color Location Convert Pixel Coordinates to Real-World Coordinates Make Measurements Distance Measurements Analytic Geometry Measurements Instrument Reader Measurements Display Results Page Calibration Perspective and Nonlinear Distortion Calibration Defining a Calibration Template Defining a Reference Coordinate System Page Page Learning Calibration Information Specifying Scaling Factors Choosing a Region of Interest Choosing a Learning Algorithm Using the Learning Score Learning the Error Map Learning the Correction Table Setting the Scaling Method Calibration Invalidation Simple Calibration Save Calibration Information Attach Calibration Information A Technical Support Resources Web Support NI Developer Zone Customer Education System Integration Worldwide Support Glossary Numbers/Symbols A B C Page D E F G H I Page J K L M N O P Page Q R S T Page Index A B C D E F G H I Page Page L M N O P R S T V W Z