National Instruments IMAQ Vision for Measurement Studio Machine Vision

©NationalInstruments Corporation 5-1 IMAQVision for LabWindows/CVI User Manual

5

Machine Vision

This chapter describeshow to perform many common machine vision

inspectiontasks. The most common inspection tasks are detecting the

presenceor absence o f parts in an image and measuring the dimensions

ofparts tosee if they meet specifications.

Measurementsare based on characteristic features of the object represented

in the image. Image processing algorithms traditionally classify the type

ofinformation contained in an image as edges, surfaces and textures, or

patterns.Different types of machine vision algorithms leverage and extract

one or more types of information.

Edgedetectors and derivative techniques—such as rakes, concentric rakes,

and spokes—use edges represented in the image. They locate with high

accuracythe position of the edge of an object in the image. For example,

youcan use the edge location to m easure the width of the part (a technique

called clamping). Youcan combine multiple edge locations to compute

intersection points, projections, circles, or ellipse fits.

Patternmatching al gorithms use edges and patterns. Pattern matching can

locatew ith veryhigh accuracy the position of fiducials or characteristic

featuresof the part under inspection. Those locations can then be combined

to computel engths, angles, and other object measurements.

The robustnessof your m easurement relies on the stability of your image

acquisition conditions. Sensor resolution, lighting, optics, vibration

control, part fixture,and general environment are key components of the

imaging setup.All the elements of the image acquisition chain directly

affectthe accuracy of the measurements.

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