National Instruments IMAQTM Improve an Image

Chapter 2 Getting Measurement-Ready Images

© National Instruments Corporation 2-9 IMAQ Vision for Visual Basic User Manual

Improve an Image

Using the information you gathered from analyzing the image, you may

want to improve the quality of the image for inspection. You can improve

the image with lookup tables, filters, grayscale morphology, and Fast

Fourier transforms (FFT).

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 methods that directly or indirectly apply lookup tables to

images:

•CWIMAQVision.MathLookup—Converts the pixel values of an

image by replacing them with values from a predefined lookup table.

IMAQ Vision has seven predefined lookup tables based on

mathematical transformations. For more information about these

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

Vision Concepts Manual.

•CWIMAQVision.UserLookup—Converts the pixel values of an

image by replacing them with values from a user-defined lookup table.

•CWIMAQVision.Equalize2—Distributes the grayscale values

evenly within a given grayscale range. Use this method to increase the

contrast in images containing few grayscale values.

•CWIMAQVision.Inverse—Inverts the pixel intensities of an image

to compute the negative of the image. For example, use this method

before applying an automatic threshold to the image if the background

pixels are brighter than the object pixels.

Filters

Filter the 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 CWIMAQVision.LowPass or define

your own lowpass filter with CWIMAQVision.Convolute or

CWIMAQVision.NthOrder.

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