National Instruments IMAQ Vision for Measurement Studio User Manual Page 22
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Chapter 2 Getting Measurement-Ready Images
© National Instruments Corporation 2-9 IMAQ Vision for LabWindows/CVI User Manual
this range in processing functions, such as determining a threshold range
during blob analysis.
If the image quality does not meet your needs, try to improve the imaging
conditions to get the desired image quality. You may need to re-evaluate
and modify each component of your imaging setup: lighting equipment
and setup, lens tuning, camera operation mode, and acquisition board
parameters. If you reach the best possible conditions with your setup but
the image quality still does not meet your needs, try to improve the image
quality using the image processing techniques described in the Improve an
Image section.
Use
imaqLineProfile()
to get the pixel distribution along a line in the
image, or use
imaqROIProfile()
to get the pixel distribution along a
one-dimensional path in the image. By looking at the pixel distribution, you
can determine if the image quality is high enough to provide you with sharp
edges at object boundaries. Also, you can determine if the image is noisy,
and identify the characteristics of the noise.
If the image quality meets your needs, use the pixel distribution information
to determine some parameters of the inspection functions you want to use.
For example, use the information from the line profile to determine the
strength of the edge at the boundary of an object. You can input this
information into
imaqEdgeTool()
to find the edges of objects along the
line.
Improve an Image
Using the information you gathered from analyzing your image, you may
want to improve the quality of your image for inspection. You can improve
your image with lookup tables, filters, grayscale morphology, and Fast
Fourier transforms.
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 functions that directly or indirectly apply lookup tables to
images:
•
imaqMathTransform()
—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
- 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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