National Instruments IMAQ Vision for Measurement Studio User Manual Page 35
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Chapter 3 Grayscale and Color Measurements
© National Instruments Corporation 3-9 IMAQ Vision for LabWindows/CVI User Manual
2. Use the entire image or regions in the image to learn the color
information using
imaqLearnColor()
, which outputs a color
spectrum that contains a compact description of the color information
that you learned. Use the color spectrum to represent the learned color
information for all subsequent matching operations. See Chapter 14,
Color Inspection,oftheIMAQ Vision Concepts Manual for more
information.
3. Define an entire image, a region, or multiple regions in an image as the
inspection or comparison area.
4. Use
imaqMatchColor()
to compare the learned color information to
the color information in the inspection regions. This function returns
an array of scores that indicates how close the matches are to the
learned color information.
5. Use the color matching score as a measure of similarity between the
reference color information and the color information in the image
regions being compared.
Learning Color Information
When learning color information, you should choose the color information
carefully:
• Specify an image or regions in an image that contain the color or color
setthatyouwanttolearn.
• Select how detailed you want the color information to be learned.
• Choose colors that you want to ignore during matching.
Choosing the Right Color Information
Because color matching only uses color information to measure similarity,
the image or regions in the image representing the object should contain
only the significant colors that represent the object, as shown in
Figure 3-5a. Figure 3-5b illustrates an unacceptable region containing
background colors.
- 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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