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National Instruments IMAQ Vision for LabWindows TM /CVI Lookup Tables, Filters

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Chapter 2 Getting Measurement-Ready Images

IMAQ Vision for LabWindows/CVI User Manual 2-10 ni.com

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

into other grayscale values in the transformed image. IMAQ Vision

provides four functions that directly or indirectly apply lookup tables to

images.

•imaqMathTransform()—Converts the pixel values of an image

byeplacing them with values from a predefined lookup table.

IMAQVision has seven predefined lookup tables based on

mathematical transformations. For more information about these

lookup tables, refer to Chapter 5, Image Processing, of the IMAQ

Vision Concepts Manual.

•imaqLookup()—Converts the pixel values of an image by replacing

them with values from a user-defined lookup table.

•imaqEqualize()—Distributes the grayscale values evenly within a

given grayscale range. Use imaqEqualize() to increase the contrast

in images containing few grayscale values.

•imaqInverse()—Inverts the pixel intensities of an image to

compute the negative of the image. For example, use imaqInverse()

before applying an automatic threshold to your image if the

background pixels are brighter than the object pixels.

Filters

Filter your image when you need to improve the sharpness of transitions in

the image or increase the overall signal-to-noise ratio of the image. You can

choose either a lowpass or highpass filter depending on your needs.

Lowpass filters remove insignificant details by smoothing the image,

removing sharp details, and smoothing the edges between the objects

and the background. You can use imaqLowPass() or define your own

lowpass filter with imaqConvolve() or imaqNthOrderFilter().

Highpass filters emphasize details, such as edges, object boundaries,

orcr acks. These details represent sharp transitions in intensity value.

Youcan define your own highpass filter with imaqConvolve() or

imaqNthOrderFilter(), or you can use a predefined highpass filter

withimaqEdgeFilter() or imaqCannyEdgeFilter(). The

imaqEdgeFilter() function allows you to find edges in an image using

predefined edge detection kernels, such as the Sobel, Prewitt, and Roberts

kernels.

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