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National Instruments IMAQ Vision for LabWindows TM /CVI Convolution Filter, Nth Order Filter, Grayscale Morphology

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Chapter 2 Getting Measurement-Ready Images

© National Instruments Corporation 2-11 IMAQ Vision for LabWindows/CVI User Manual

Convolution Filter

The imaqConvolve() function allows you to use a predefined set of

lowpass and highpass filters. Each filter is defined by a kernel of

coefficients. Use imaqGetKernel() to retrieve predefined kernels. If the

predefined kernels do not meet your needs, define your own custom filter

using a 2D array of floating point numbers.

Nth Order Filter

The imaqNthOrderFilter() function allows you to define a lowpass or

highpass filter depending on the value of N that you choose. One specific

Nth order filter, the median filter, removes speckle noise, which appears as

small black andwhite dots. Use imaqMedianFilter() to apply a median

filter. For more information about Nth order filters, refer to Chapter 5,

Image Processing, of the IMAQ Vision Concepts Manual.

Grayscale Morphology

Perform grayscale morphology when you want to filter grayscale

features of an image. Grayscale morphology helps you remove or

enhance isolated features, such as bright pixels on a dark background.

Use these transformations on a grayscale image to enhance non-distinct

features before thresholding the image in preparation for particle analysis.

Grayscale morphological transformations compare a pixel to those pixels

surrounding it. The transformation keeps the smallest pixel values when

performing an erosion or keeps the largest pixel values when performing

ad ilation.

Refer to Chapter 5, Image Processing, of the IMAQ Vision Concepts

Manual for more information about grayscale morphology

transformations.

Use imaqGrayMorphology() toperform one of the following seven

transformations:

• Erosion—Reduces the brightness of pixels that are surrounded by

neighbors with a lower intensity.

• Dilation—Increases the brightness of pixels surrounded by neighbors

with a higher intensity. A dilation produces the opposite effect of an

erosion.

• Opening—Removes bright pixels isolated in dark regions and smooths

boundaries.

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