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National Instruments IMAQ Vision for LabWindows TM /CVI Using Multiple Regions in the Image

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Chapter 3 Making Grayscale and Color Measurements

© National Instruments Corporation 3-11 IMAQ Vision for LabWindows/CVI User Manual

Figure 3-8. 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.

Figure3-9 shows how light reflects differently off of the 3D surfaces of the

fuses, resulting in slightly different colors for identical fuses. Compare the

3-amp fuse in the upper row with the 3-amp fuse in the lower row. The

difference in light reflection results in different color spectrums for

identical fuses.

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—close to 1000. But

the match score for the 3-amp fuse in the lower row is quite low—around

500. This problem could cause a mismatch for the color matching in a fuse

box inspection process.

The color learning algorithm of IMAQ Vision uses a clustering process to

find therepresentative colors from the color information specified by one

or multiple regions in the image. To create a representative color spectrum

for all 3-amp fuses in the learning phase, draw an ROI around the 3-amp

fuse in the upper row, hold down the <Shift> key, and draw another ROI

around the 3-amp fuse in the lower row. The new color spectrum represents

3-amp fuses better and results in high match scores (around 800) for both

3-amp fuses. Use as many samples as you want in an image to learn the

representative color spectrum for a specified template.

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