National Instruments IMAQ Vision for LabWindows TM /CVI FFT

Chapter 2 Getting Measurement-Ready Images

IMAQ Vision for LabWindows/CVI User Manual 2-12 ni.com

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

boundaries.

• Proper-opening—Removes bright pixels isolated in dark regions and

smooths the inner contours of particles.

• Proper-closing—Removes dark pixels isolated in bright regions and

smooths the inner contours of particles.

• Auto-median—Generates simpler particles that have fewer details.

FFT

Use the Fast Fourier Transform (FFT) to convert an image into its

frequency domain. In an image, details and sharp edges are associated

with mid to high spatial frequencies because they introduce significant

gray-level variations over short distances. Gradually varying patterns are

associated with low spatial frequencies.

An image can have extraneous noise, such as periodic stripes, introduced

during the digitization process. In the frequency domain, the periodic

pattern is reduced to a limited set of high spatial frequencies. Also, the

imaging setup may produce non-uniform lighting of the field of view,

which produces an image with a light drift superimposed on the

information you want to analyze. In the frequency domain, the light drift

appears as a limited set of low frequencies around the average intensity of

the image, the DC component.

You can use algorithms working in the frequency domain to isolate and

remove these unwanted frequencies from your image. Complete the

following steps to obtain an image in which the unwanted pattern has

disappeared but the overall features remain.

1. Use imaqFFT() to convert an image from the spatial domain to the

frequency domain. This function computes the FFT of the image and

results ina complex image representing the freque ncy information of

your image.

2. Improve your image in the frequency domain with a lowpass or

highpass frequency filter. Specify which type of filter to use with

imaqAttenuate() or imaqTruncate(). Lowpass filters smooth

noise, details, textures, and sharp edges in an image. Highpass filters

emphasize details, textures, andsharp edges in images, but they also

emphasize noise.

• Lowpass attenuation—The amount of attenuation is directly

proportional to the frequency information. At low frequencies,

there is little attenuation. As the frequencies increase, the

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