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Chapter 4 Blob Analysis
© National Instruments Corporation 4-3 IMAQ Vision for LabWindows/CVI User Manual
Improve the Binary Image
After you threshold your image, you may want to improve the resulting
binary image with binary morphology. You can use primary binary
morphology or advanced binary morphology to remove unwanted blobs,
separate connected blobs, or improve the shape of blobs. Primary
morphology functions work on the image as a whole by processing pixels
individually. Advanced morphology operations are built upon the primary
morphological operators and work on particles as opposed to pixels.
Note
The terms blob and particle are used interchangeably in this chapter.
The advanced morphology functions that improve binary images require
that you specify the type of connectivity to use. Connectivity specifies how
IMAQ Vision determines whether two adjacent pixels belong to the same
particle. If you have a blob that contains narrow areas, use connectivity-8
to ensure that the software recognizes the connected pixels as one blob.
If you have two blobs that touch at one point, use connectivity-4 to ensure
that the software recognizes the pixels as two separate blobs. For more
information about connectivity, see Chapter 9, Binary Morphology,ofthe
IMAQ Vision Concepts Manual.
Note
Use the same type of connectivity throughout your application.
Removing Unwanted Blobs
Use
imaqRejectBorder()
to remove blobs that touch the border of the
image. Reject blobs on the border of the image when you suspect that the
information about those blobs is incomplete.
Use
imaqSizeFilter()
to remove large or small particles that do not
interest you. You can also use the
IMAQ_ERODE
,
IMAQ_OPEN
,and
IMAQ_POPEN
methods in
imaqMorphology()
to remove small particles.
Unlike
imaqSizeFilter()
, these three operations alter the size and
shape of the remaining blobs.
Use the
IMAQ_HITMISS
method of
imaqMorphology()
to locate
particular configurations of pixels, which you define with a structuring
element. Depending on the configuration of the structuring element, the
IMAQ_HITMISS
method can locate single isolated pixels, cross-shape or
longitudinal patterns, right angles along the edges of particles, and other
user-specified shapes. For more information about structuring elements,
see Chapter 9, Binary Morphology,oftheIMAQ Vision Concepts Manual.
- 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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