National Instruments IMAQ Vision for LabWindows TM /CVI - page 106

Glossary

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

line gauge Measures the distance between selected edges with high-precision subpixel

accuracy along a line in an image. For example, this function can be used

to measure distances between points and edges. This function also can step

and repeat its measurements across the image.

line profile Represents the gray-level distribution along a line of pixels in an image.

linear filter A special algorithm that calculates the value of a pixel based on its own

pixel value as well as the pixel values of its neighbors. The sum of this

calculation is divided by the sum of the elements in the matrix to obtain a

new pixel value.

logarithmic function Increases the brightness and contrast in dark regions of an image and

decreases the contrast in bright regions of the image.

logic operators The image operations AND, NAND, OR, XOR, NOR, XNOR, difference,

mask, mean, max, and min.

lossless compression Compression in which the decompressed image is identical to the original

image.

lossy compression Compression in which the decompressed image is visually similar but not

identical to the original image.

lowpass attenuation Applies a linear attenuation to the frequencies in an image, with no

attenuation at the lowest frequency and full attenuation at the highest

frequency.

lowpass FFT filter Removes or attenuates high frequencies present in the FFT domain of an

image.

lowpass filter Attenuates intensity variations in an image. You can use these filters to

smooth an image by eliminating fine details and blurring edges.

lowpass frequency

filter

Attenuates high frequencies present in the frequency domain of the image.

A lowpass frequency filter suppresses information related to fast variations

of light intensities in the spatial image.

lowpass truncation Removes all frequency information above a certain frequency.

L-skeleton function Uses an L-shaped structuring element in the skeleton function.

luma The brightness information in the video picture. The luma signal amplitude

varies in proportion to the brightness of the video signal and corresponds

exactly to the monochrome picture.

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