National Instruments IMAQ Vision for Measurement Studio Improve an Image

Chapter2 GettingMeasurement-Ready Images

©NationalInstruments Corporation 2-9 IMAQVision for LabWindows/CVI User Manual

this range in processing functions, such as determining a threshold range

during blob analysis.

If the image quality does not meet your needs, try to improve the imaging

conditions to get the desired image quality.You may need to re-evaluate

and modify each component ofyour imaging setup: lighting equipment

and setup, lenstuning, camera operation mode, and acquisition board

parameters.If you reach the best possible conditions with your setup but

the image quality still does not meet your needs, try to improvethe image

quality using the image processing techniques described in the Improve an

Image section.

Use imaqLineProfile()to get the pixel distribution along a line in the

image,or useimaqROIProfile() to get the pixel distribution along a

one-dimensionalpath in the image. By looking at the pixel distribution, you

candetermine if the image quality is high enough to provide you with sharp

edges at object boundaries. Also, you can determine if the image is noisy,

andidentify thecharacteristics of the noise.

Ifthe image quality meets your needs, use the pixel distribution information

todetermine some parameters o f the inspection functions you want to use.

Forexample, use the infor mation from the line profile to determine the

strength of the edge at the boundary of an object. Youcan input this

informationinto imaqEdgeTool() to find the edges of objects along the

line.

Improve an Image

Usingthe information you gathered from analyzing your image, you may

wantto improve the quality of your image for inspection. You can improve

your image with lookup tables, filters, grayscale morphology, and Fast

Fourier transforms.

Lookup Tables

Apply lookup table (LUT) transformations to highlight image details in

areas containing significant information at the expense of other areas.

A LUT transformation converts input grayscale values in the source image

intoother grayscale values in the transformed image. IMAQ Vision

provides four functions that directly or indirectly apply lookup tables to

images:

•imaqMathTransform()—Convertsthe pixel values of an image by

replacing them with valuesfrom a predefined lookup table. IMAQ

Visionhas seven predefined lookup tables based on mathematical

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