National Instruments IMAQTM - page 107

Glossary

IMAQ Vision for Visual Basic User Manual G-10 ni.com

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

an ew 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.

luminance See luma.

LUT Lookup table. A table containing values used to transform the gray-level

values of an image. For each gray-level value in the image, the

corresponding new value is obtained from the lookup table.

Contents

Main Page Important Information Warranty Copyright Trademarks WARNING REGARDING USE OF NATIONAL INSTRUMENTS PRODUCTS 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 Page 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 Documentation and Examples IMAQ Vision for Visual Basic Organization cwimaq.ocx CWIMAQ Control CWIMAQVision Control CWIMAQViewer Control niocr.ocx NIOCR control cwmv.ocx CWMachineVision control ActiveX Objects Creating IMAQ Vision Applications Chapter 1 Introduction to IMAQ Vision Figure 1-1. General Steps for Designing a Vision Application IMAQ Vision for Visual Basic User Manual 1-6 ni.com Chapter 1 Introduction to IMAQ Vision Figure 1-2. Inspection Steps for Building a Vision Application National Instruments Corporation 1-7 IMAQ Vision for Visual Basic User Manual Getting Measurement-Ready Images Set Up Your Imaging System Calibrate Your Imaging System Create an Image Page Acquire or Read an Image Acquiring an Image One-Shot Acquisition Continuous Acquisition Reading a File Converting an Array to an Image Display an Image Attach Calibration Information Analyze an Image Page 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 Figure 3-2. Tools Information Defining Regions Programmatically Defining Regions with Masks Measure Grayscale Statistics Measure Color Statistics Figure 3-3. Primary Components of an 32-bit Color Image Figure 3-4. Primary Components of a 64-bit Color Image CWIMAQVision.ColorValueConversion2. 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 Using Edge Detection to Build a Coordinate Transformation Page Using Pattern Matching to Build a Coordinate Transformation Page Chapter 5 Performing Machine Vision Tasks National Instruments Corporation 5-7 IMAQ Vision for Visual Basic User Manual Choosing a Method to Build the Coordinate Transformation Figure 5-4. Building a Coordinate Transformation 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 Effective 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 Effective 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 Read 1D Barcodes Read Data Matrix Barcode Read PDF417 Barcode 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 Mode Calibration Invalidation Simple Calibration Save Calibration Information Attach Calibration Information Page 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 Page Index Numerics A B C D E F G H I K L M N O P R S T U V W Z