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National Instruments IMAQ Vision for Measurement Studio Using the Learning Score

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Chapter6 Calibration

©NationalInstruments Corporation 6-7 IMAQVision for LabWindows/CVI User Manual

IMAQ_PERSPECTIVEto choose the perspective calibration algorithm.

Learning and applying perspectiveprojection is l ess computationally

intensivethan the nonlinear method. However, perspective projection

cannot handle nonlinear distortions.

If your imaging setup exhibits nonlinear distortion, use the nonlinear

method.The nonlinear method guarantees accurate results only in the area

thatthe calibration gridc overs,as shown in Figure 6-6. If your system

exhibitsboth perspective and nonlinear distortion, use the nonlinear

methodto correct for both. Set the mode element of the options parameter

to IMAQ_NONLINEARto choose the non linear calibration algorithm.

Figure 6-6. CalibrationROIs

Using the Learning Score

The learning process returns a score that reflects how well the software

learnedthe input image. A high learning score indicates thatyou chose the

the appropriate learning algorithm, that the grid image complies with the

guideline, and that your vision system setup is adequate.

Ift he learning process returns a lowscore, try the following:

1. Makesure your grid com plies 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 softwaremay estimate the center of the dots incorrectly.

1 CalibrationROI Using the

PerspectiveA lgorithm

2 CalibrationROI Using the Nonlinear

Algorithm

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2

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