Manuals / Brands / Computer Equipment / Network Card / National Instruments / Computer Equipment / Network Card

National Instruments IMAQ Vision for Measurement Studio Improve the Binary Image

1 114

Download 114 pages, 1.93 Mb

Chapter4 BlobAnalysis

©NationalInstruments Corporation 4-3 IMAQVision for LabWindows/CVI User Manual

Improve the Binary Image

After you threshold your image, you may want to improve the resulting

binary image with binary morphology. You can use primary binary

morphology or advanced binary morphology to remove unwanted blobs,

separate connected blobs, or improve the shape of blobs. Primary

morphology functions work on the image as a whole by processing pixels

individually.A dvanced morphology operations are built upon the primary

morphological operators and work on particles as opposed to pixels.

Note The terms blob and particle are used interchangeably in this chapter.

The advancedmorphology functions that im provebinary images require

thatyou specify the type of connectivity to use. Connectivity specifies how

IMAQVision determines whether two adjacent pixels belong to the same

particle. If you havea blob that contains narrow areas, use connectivity-8

to ensure thatthe software recognizes the connected pixels as one blob.

Ify ou havetwo blobs that touch at one point , use connectivity-4t o ensure

thatthe software recognizes the pixels as two separate blobs. For more

information about connectivity,see Chapter 9, Binary Morphology,ofthe

IMAQ Vision Concepts Manual.

Note Use the same type of connectivity throughout your application.

Removing Unwanted Blobs

Use imaqRejectBorder()to remove blobs th at touch the border of the

image. Reject blobs on the border of the image when you suspect that the

information about those blobs is incomplete.

Use imaqSizeFilter()to remove large or small particles that do not

interest you. Youcan also use the IMAQ_ERODE,IMAQ_OPEN,and

IMAQ_POPENmethods i n imaqMorphology()to rem ovesmall particles.

UnlikeimaqSizeFilter(), these three ope rations alter the size and

shape of the remaining blobs.

Use the IMAQ_HITMISSmethod of imaqMorphology() to locate

particular configurationsof pixels, which you define with a structuring

element. Depending on the configuration of the structuring element, the

IMAQ_HITMISSmethod can locate single isolated pixels, cross-shape or

longitudinal patterns, right angles along the edges of particles, and other

user-specified shapes.For more information about structuring elements,

seeChap ter 9, BinaryMorphology,oftheIMAQVision Concepts Manual.

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