National Instruments IMAQ Vision for Measurement Studio User Manual Page 24
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Chapter 2 Getting Measurement-Ready Images
© National Instruments Corporation 2-11 IMAQ Vision for LabWindows/CVI User Manual
Nth Order Filter
The
imaqNthOrderFilter()
function allows you to define a lowpass or
highpass filter depending on the value of N that you choose. One specific
Nth order filter, the median filter, removes speckle noise, which appears as
small black and white dots. Use
imaqMedianFilter()
to apply a median
filter. For more information about Nth order filters, see Chapter 5, Image
Processing,oftheIMAQ Vision Concepts Manual.
Grayscale Morphology
Perform grayscale morphology when you want to filter grayscale
features of an image. Grayscale morphology helps you remove or
enhance isolated features, such as bright pixels on a dark background.
Use these transformations on a grayscale image to enhance non-distinct
features before thresholding the image in preparation for blob analysis.
Use
imaqGrayMorphology()
to perform one of the following seven
transformations:
• Erosion—Reduces the brightness of pixels that are surrounded by
neighbors with a lower intensity.
• Dilation—Increases the brightness of pixels surrounded by neighbors
with a higher intensity. A dilation has the opposite effect as an erosion.
• Opening—Removes bright pixels isolated in dark regions and smooths
boundaries.
• Closing—Removes dark pixels isolated in bright regions and smooths
boundaries.
• Proper-opening—Removes bright pixels isolated in dark regions and
smooths the inner contours of particles.
• Proper-closing—Removes dark pixels isolated in bright regions and
smooths the inner contours of particles.
• Auto-median—Generates simpler particles that have fewer details.
For more information about grayscale morphology transformations, see
Chapter 5, Image Processing,oftheIMAQ Vision Concepts Manual.
FFT
Use the Fast Fourier Transform (FFT) to convert an image into its
frequency domain. In an image, details and sharp edges are associated
with mid to high spatial frequencies because they introduce significant
gray-level variations over short distances. Gradually varying patterns are
associated with low spatial frequencies.
- 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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