National Instruments IMAQ Vision for Measurement Studio User Manual Page 11
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Chapter 1 Introduction to IMAQ Vision
IMAQ Vision for LabWindows/CVI User Manual 1-4 ni.com
IMAQ Machine Vision Function Tree
The IMAQ Machine Vision function tree (
NIMachineVision.fp
)
contains separate classes corresponding to groups or types of functions.
Table 1-2 lists the IMAQ Machine Vision function types and gives a
description of each type.
Frequency
Domain
Analysis
Functions for the extraction and manipulation of complex planes. Functions of
this type perform FFTs, inverse FFTs, truncation, attenuation, addition,
subtraction, multiplication, and division of complex images.
Barcode A function that reads a barcode.
LCD Functions that find and read seven-segment LCD characters.
Meter Functions that return the arc information of a meter and read the meter.
Utilities Functions that return structures, and a function that returns a pointer to
predefined convolution matrices.
Obsolete Functions that are no longer necessary but may exist in older applications.
Table 1-2. IMAQ Machine Vision Function Types
Function Type Description
Coordinate Transform Functions that find coordinate transforms based on image contents.
Count and Measure Objects A function that counts and measures objects in an image.
Find Patterns A function that finds patterns in an image.
Locate Edges Functions that locate different types of edges in an image.
Measure Distances Functions that measure distances between objects in an image.
Measure Intensities Functions that measure light intensities in various shaped regions
within an image.
Select Region of Interest Functions that allow a user to select a specific region of interest in
an image.
Table 1-1. IMAQ Vision Function Types (Continued)
Function Type Description
- 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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