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National Instruments IMAQ Vision for Measurement Studio Attach Calibration Information, Analyze an Image

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Chapter2 GettingMeasurement-Ready Images

IMAQVision for LabWindows/CVI User Manual 2-8 ni.com

Attach Calibration Information

If you want to attach the calibration information of the current setup to

each imagey ou acquire, useimaqCopyCalibrationInfo().This

functiontakes in a source image containing the cali bration information and

ad estination image that you want to calibrate. The output image is your

inspectionimage with the calibration information attached to it. For

detailed information about calibration, see Chapter 6, Calibration.

Note Because calibration information is part of the image, it is propagated throughout

the processing and analysis of the image. Functions that modify the image size (such as

geometricaltransforms) void the calibration information. Use imaqWriteVisionFile()

to savethe image and all of the attached calibration informat ion to a file.

Analyze an Image

Once you acquireand display an image, you may want to analyze the

contents of the image for the following reasons:

•To determine whether the image quality is high enough for your

inspectiontask.

•Toobtain t he values of parameters that you want to use in processing

functions during the inspection process.

The histogram and line profile tools can help you analyze the quality of

your images.

UseimaqHistogram() to analyze the overall grayscale distribution in the

image. Use the histogram of the image to analyze two important criteria

thatdefine the quality of an imag e—saturation and contrast. If your image

is underexposed(do es not haveenough light) the majority o f your pixels

willhave low intensity values, which appear as a concentration of peaks on

theleft side of your histogram. If your image is overexposed (has too much

light) the majority of your pixels will have ahi gh intensity values, which

appearas aco ncentration of peaks on the right side of your histogram. If

yourimage has an appropriate amount of contrast, your histogram will have

distinct regionso f pixelconcentrations. Use th eh istogram information to

decide if the image quality is high enough to separate objects of interest

from the background.

Ifthe image quality meets your needs, use the histogram to determine the

rangeo fpixel values that correspond to objects in the image. You can use

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