National Instruments IMAQ Vision for Measurement Studio User Manual Page 59
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Chapter 5 Machine Vision
© National Instruments Corporation 5-13 IMAQ Vision for LabWindows/CVI User Manual
Finding Points Using Pattern Matching
The pattern matching algorithms in IMAQ Vision measure the similarity
between an idealized representation of a feature, called a template, and the
feature that may be present in an image. A feature is defined as a specific
pattern of pixels in an image. Pattern matching returns the location of the
center of the template and the template orientation. Follow these
generalized steps to find features in an image using pattern matching:
1. Define a reference or fiducial pattern in the form of a template image.
2. Use the reference pattern to train the pattern matching algorithm with
imaqLearnPattern()
.
3. Define an image or an area of an image as the search area. A small
search area reduces the time to find the features.
4. Set the tolerances and parameters to specify how the algorithm
operates at run time using the options parameter of
imaqMatchPattern()
.
5. Test the search algorithm on test images using
imaqMatchPattern()
.
6. Verify the results using a ranking method.
Defining and Create Good Template Images
The selection of a good template image plays a critical part in obtaining
good results. Because the template image represents the pattern that you
want to find, make sure that all the important and unique characteristics of
the pattern are well defined in the image.
Several factors are critical in creating a template image. These critical
factors include symmetry, feature detail, positional information, and
background information.
Symmetry
A rotationally symmetric template is less sensitive to changes in rotation
than one that is rotationally asymmetric. A rotationally symmetric template
provides good positioning information but no orientation information.
- IMAQ Vision 1
- User Manual 1
- Important Information 3
- Conventions 4
- Contents 5
- Chapter 4 6
- Chapter 5 6
- Introduction to IMAQ Vision 8
- IMAQ Vision Function Tree 9
- Table 1-1 10
- IMAQ Vision Function Types 10
- NIMachineVision.fp 11
- Getting Measurement-Ready 14
- Calibrate Your Imaging System 15
- Create an Image 15
- Source and Destination Images 17
- AcquireorReadanImage 18
- Acquiring an Image 19
- Reading a File 19
- Display an Image 20
- Analyze an Image 21
- Improve an Image 22
- Convolution Filter 23
- Nth Order Filter 24
- Grayscale Morphology 24
- Complex Image Operations 26
- Grayscale and Color 27
- Measurements 27
- Figure 3-2. ROI Constructor 30
- Defining Regions with Masks 32
- Measure Grayscale Statistics 33
- Measure Color Statistics 33
- Comparing Colors 34
- Learning Color Information 35
- Using the Entire Image 36
- Using a Region in the Image 36
- Ignoring Learned Colors 39
- Blob Analysis 40
- Correct Image Distortion 41
- Create a Binary Image 41
- Improve the Binary Image 42
- Make Particle Measurements 43
- Chapter 4 Blob Analysis 44
- Machine Vision 47
- Locate Objects to Inspect 48
- Chapter 5 Machine Vision 49
- Figure 5-3 51
- IMAQ_MATCH_ROTATION_INVARIANT 52
- IMAQ_MATCH_SHIFT_INVARIANT 52
- IMAQ_FIND_REFERENCE 52
- IMAQ_UPDATE_TRANSFORM 52
- Set Search Areas 54
- Find Measurement Points 55
- Symmetry 59
- Feature detail 60
- Positional information 60
- Defining a Search Area 61
- Match Mode 63
- Minimum Contrast 63
- Rotation Angle Ranges 63
- Color Information 65
- Feature Detail 66
- Positional Information 66
- Background Information 66
- Color Sensitivity 69
- Search Strategy 69
- Color Score Weight 70
- Make Measurements 72
- Display Results 74
- Calibration 76
- CoordinateSystem 77
- Chapter 6 Calibration 78
- Specifying Scaling Factors 81
- Choosing a Region of Interest 81
- Choosing a Learning Algorithm 81
- Using the Learning Score 82
- Learning the Error Map 83
- Learning the Correction Table 83
- Setting the Scaling Method 83
- Calibration Invalidation 83
- Simple Calibration 84
- Save Calibration Information 85
- Technical Support Resources 86
- Worldwide Support 87
- Glossary 100
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