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National Instruments IMAQ Vision for Measurement Studio Color Sensitivity, Search Strategy

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Chapter5 MachineVision

©NationalInstruments Corporation 5-23 IMAQVision for LabWindows/CVI User Manual

The followingare some elements in the IM AQVision pattern matching

algorithm and howthey influence pattern matchin g:c olor sensitivity,

search strategy,color score weight, ignore background colors, minimum

contrast, and rotation angle ranges.

Color Sensitivity

Use the sensitivityelement to control the granularity of the color

information in the template image. If the background and objects in the

imagecontain colors that are very close to colors in the template image, use

ahigher color sensitivi ty setting. A higher sensitivity setting distinguishes

colors with very close hue values. Three color sensitivity settings are

available in IMAQ Vision: IMAQ_SENSITIVITY_LOW,

IMAQ_SENSITIVITY_MED,andIMAQ_SENSITIVITY_HIGH.Usethe

defaultLow setting if the colors in the template are very different from t he

colors in the background or other objects that you are not interested in.

Increasethe color sensitivity settings as the color differences decrease. For

moreinformation on color sensiti vity, see Chapter 14, Color Inspection,of

the IMAQ Vision Concepts Manual.

Search Strategy

Use the strategyelement t o optimize the speed of the color pattern

matching algorithm. The search strategy controls the step size,

sub-sampling factor, percentage of color information used from the

template.

Choosefrom fourdifferent strategies:

•IMAQ_VERY_AGGRESSIVE—Usesthe largest step size, the most

sub-samplingand only the dominant colo r from the template to search

for the template. Use this strategywhen the colo r in the template is

almost uniform, the template is well contrasted from the background

andthere is a good amount of separation between different occurrences

of the template in the image. This strategy is the fastest way to find

templatesin an image.

•IMAQ_AGGRESSIVE—Usesa large step size, a lot of sub-sampling,

and little of the color information from the template.

•IMAQ_BALANCED—Usesvalues in between the IMAQ_AGGRESSIVE

and IMAQ_CONSERVATIVEstrategies.

•IMAQ_CONSERVATIVE—Usesa very small step size, a sub-sampling

factorof two, and all the color information present in the template.

Thisstrategy is the most reliable met hod to look for a template in any

image at potentially reducedspeed.

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