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National Instruments IMAQ Vision for LabWindows TM /CVI Learning the Error Map, Learning the Correction Table, Setting the Scaling Method, Calibration Invalidation

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Chapter 6 Calibrating Images

IMAQ Vision for LabWindows/CVI User Manual 6-8 ni.com

If the learning process returns a learning score below 600, try the following:

1. Make sure your grid complies with the guidelines listed in the

Defining a Calibration Template section of this chapter.

2. Check the lighting conditions. If you have too much or too little

lighting, the software may estimate the center of the dots incorrectly.

Also, adjust the range parameter to distinguish the dots from the

background.

3. Select another learning algorithm. When nonlinear lens distortion is

present, using perspective projection sometimes results in a low

learning score.

Learning the Error Map

An error map helps you gauge the quality of your complete system.

The error map returns an estimated error range to expect when a pixel

coordinate is transformed into a real-world coordinate. The transformation

accuracy may be higher than the value the error range indicates. Set the

learnMap element of the options parameter to TRUE to learn the error

map.

Learning the Correction Table

If the speed of image correction is a critical factor for your application, use

a correction table. The correction table is a lookup table stored in memory

that contains the real-world location information of all the pixels in the

image. The extra memory requirements for this option are based on the size

of the image. Use this option when you want to correct several images at a

time in your vision application. Set the learnTable element of the

options parameter to TRUE to learn the correction table.

Setting the Scaling Method

Use the method element of the options parameter to choose the appearance

of the corrected image. Select either IMAQ_SCALE_TO_FIT or

IMAQ_SCALE_TO_PRESERVE_AREA. Refer to Chapter 3, System Setup and

Calibration, of the IMAQ Vision Concepts Manual for more information

about the scaling methods.

Calibration Invalidation

Any image processing operation that changes the image size or orientation

voids the calibration information in a calibrated image. Examples of

functions that void calibration information include imaqResample(),

imaqScale(), imaqArrayToImage(), and imaqUnwrap().

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