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National Instruments IMAQ Vision for LabWindows TM /CVI Testing the Search Algorithm on Test Images, Finding Points Using Color Location

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Chapter 5 Performing Machine Vision Tasks

© National Instruments Corporation 5-25 IMAQ Vision for LabWindows/CVI User Manual

Rotation Angle Ranges

Refer to the Setting Matching Parameters and Tolerances section of this

chapter for information about rotation angle ranges.

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 imaqMatchColorPattern(). These test images reflect the

images generated by your machine vision application during true operating

conditions. If the 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 when your application has the

following characteristics:

• Requires the location and the number of regions in an image with their

specific color information

• Relies on the cumulative color information in the region, instead of the

color arrangement in the region

• Does not require the orientation of the region

• Does not always require the location with sub-pixel accuracy

• Does not require shape information for the region

Complete the following general 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

imaqLearnColorPattern().

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 the featureMode element of the imaqMatchColorPattern()

options parameter to IMAQ_COLOR_FEATURES.

5. Set the tolerances and parameters to specify how the algorithm

operates atr un time using the options parameter of

imaqMatchColorPattern().

Contents

Main IMAQ Page Important Information Warranty Copyright Trademarks Patents Contents About This Manual Chapter 1 Introduction to IMAQ Vision Chapter 2 Getting Measurement-Ready Images Chapter 3 Making Grayscale and Color Measurements Chapter 4 Performing Particle Analysis Chapter 5 Performing Machine Vision Tasks Chapter 6 Calibrating Images Page About This Manual Conventions Related Documentation IMAQ Vision NI Vision Assistant NI Vision Builder for Automated Inspection Other Documentation Introduction to IMAQ Vision About IMAQ Vision Application Development Environments IMAQ Vision Function Tree IMAQ Machine Vision Function Tree Creating IMAQ Vision Applications Chapter 1 Introduction to IMAQ Vision National Instruments Corporation 1-5 IMAQ Vision for LabWindows/CVI User Manual Chapter 1 Introduction to IMAQ Vision IMAQ Vision 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 Acquire or Read an Image 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 Complex Image Operations Making Grayscale and Color Measurements Define Regions of Interest Defining Regions Interactively Page Page Page Tools Palette Transformation Defining Regions Programmatically Defining Regions with Masks Measure Grayscale Statistics Measure Color Statistics Figure 3-4. Primary Components of a 32-Bit Color Image Figure 3-5. Primary Components of a 64-Bit Color Image Note You can also use imaqExtractColorPlanes() to process the red, green, and blue components of a 64-bit image. Comparing Colors Learning 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 Performing Particle Analysis Create a Binary Image Improve the Binary Image Removing Unwanted Particles Separating Touching Particles Improving Particle Shapes Make Particle Measurements Page Page Page Page Page Page Performing Machine Vision Tas ks Locate Objects to Inspect Page Using Edge Detection to Build a Coordinate Transform Page Using Pattern Matching to Build a Coordinate Transform Chapter 5 Performing Machine Vision Tasks National Instruments Corporation 5-7 IMAQ Vision for LabWindows/CVI User Manual Choosing a Method to Build the Coordinate Transform Figure 5-4. Building a Coordinate Transform Set Search Areas Defining Regions Interactively Defining Regions Programmatically Find Measurement Points Finding Features Using Edge Detection Finding Lines or Circles Finding Edge Points Along One Search Contour Finding Edge Points Along Multiple Search Contours Finding Points Using Pattern Matching Defining and Creating Good Template Images Symmetry Feature Detail Positional Information Background Information Training the Pattern Matching Algorithm Defining a Search Area Setting Matching Parameters and Tolerances Match Mode Minimum Contrast Rotation Angle Ranges Testing the Search Algorithm on Test Images Using a Ranking Method to Verify Results Finding Points Using Color Pattern Matching Defining and Creating Good Color Template Images Color Information Symmetry Feature Detail Positional Information Training the 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 Testing the 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 Identify Parts Under Inspection Classifying Samples Reading Characters Reading Barcodes Reading 1D Barcodes Reading Data Matrix Barcodes Reading PDF417 Barcodes Display Results Page Page Calibrating Images Perspective and Nonlinear Distortion Calibration Defining a Calibration Template Defining a Reference Coordinate System 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 and Professional Services Glossary Numbers A B Page C D E F G H I Page J K L Page M N O P Q R S T V Index Numerics A B C D E F G H I K L M N O P R S T U V W