National Instruments IMAQ Vision for Measurement Studio User Manual Page 100
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Glossary
© National Instruments Corporation G-13 IMAQ Vision for LabWindows/CVI User Manual
match score A number ranging from 0 to 1000 that indicates how closely an acquired
image matches the template image. A match score of 1000 indicates a
perfect match. A match score of 0 indicates no match.
median filter A lowpass filter that assigns to each pixel the median value of its neighbors.
This filter effectively removes isolated pixels without blurring the contours
of objects.
memory buffer See buffer.
morphological
transformations
Extract and alter the structure of objects in an image. You can use these
transformations for expanding (dilating) or reducing (eroding) objects,
filling holes, closing inclusions, or smoothing borders. They are used
primarily to delineate objects and prepare them for quantitative inspection
analysis.
MSB Most significant bit.
M-skeleton function Uses an M-shaped structuring element in the skeleton function.
N
neighbor A pixel whose value affects the value of a nearby pixel when an image is
processed. The neighbors of a pixel are usually defined by a kernel or a
structuring element.
neighborhood
operations
Operations on a point in an image that take into consideration the values of
the pixels neighboring that point.
NI-IMAQ Driver software for National Instruments IMAQ hardware.
nonlinear filter Replaces each pixel value with a nonlinear function of its surrounding
pixels.
nonlinear gradient filter A highpass edge-extraction filter that favors vertical edges.
nonlinear Prewitt filter A highpass, edge-extraction filter based on two-dimensional gradient
information.
nonlinear Sobel filter A highpass, edge-extraction filter based on two-dimensional gradient
information. The filter has a smoothing effect that reduces noise
enhancements caused by gradient operators.
- 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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