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July 27, 2002 Image Processing for K.R. Precision 1
Image Processing Training Image Processing Training Lecture 1Lecture 1
by Suthep Madarasmi, Ph.D.
Assistant Professor
Department of Computer Engineering
King Mongkut’s University of Technology Thonburi
July 27, 2002 Image Processing for K.R. Precision 2
Presentation Overview Presentation Overview
1.0 Image Formation1.1 Image Formats and Types1.2 Image Formation Models1.3 Coordinate Transformations 1.4 Camera Calibration
2.0 Binary Images 2.1 Histograms 2.2 Thresholding Techniques2.3 Example: Rubber Inspection2.4 Binary Image Features2.5 Measurement and Accuracy
3.0 Image Filtering3.1 Convolution Operation3.2 Common Filters3.3 Template Matching3.4 Edge Detection3.4 Example: Paper 3.6 Texture Features 3.7 Example: Food
Introduction
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Input Image
Output Image
Image Precessing
Image Processing:
• Image Enhancement
• Edge Finding
• Image Segmentation
Machine Vision:
• Scene Description
• Shape Information
• Object Recognition
What is Image ProcessingWhat is Image Processing
Introduction
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Image Formation ModelImage Formation Model
1.0 Image Formation
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Image Projection ModelImage Projection Model
Z
Xf
xx = Xf/Z; y = Yf/Z
1.0 Image Formation
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Digital Image RepresentationDigital Image Representation
1.0 Image Formation
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Images Spatial ResolutionImages Spatial Resolution
1.0 Image Formation
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Gray Level ResolutionsGray Level Resolutions
1.0 Image Formation
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Digital Image FormatsDigital Image Formats
RGB Images CMYK Images 256 Indexed Color Images 16 Indexed Color Images Gray Scale Images (8 bit) Gray Scale Images (4 bit) Black and White Images Image Types:
GIF, JPG, BMP, TIF, Multi-Page TIF, PDF, PS, RTF, etc.
1.1 Image Formats and Types
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Coordinate TransformationsCoordinate Transformations
3-D Transformations: Rotation & Translation Object Coordinates 3D World Coordinates 3D Camera Coordinates 3D Image Coordinates 2D, Continuous Cartesian Image Coordinates 2D, Discreet Cartesian (x, y) Image Coordinates 2D, Device Independent (r, c) Image Coordinates 2D, Device Coordinates (x, y)
1.3 Coordinate Transformations
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Camera CalibrationCamera Calibration
1.4 Camera Calibration
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Binary Image Assumed from Binary Image Assumed from 2 Sources with Gaussian 2 Sources with Gaussian NoiseNoise
2.0 Binary Images
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Image HistogramImage Histogram
A histogram is the count of each gray scale within the image
Histogray may represent P[i], where i is gray value between 0..255.
Histograms are used to look at gray scale distribution for thresholding to binary image
Examples of Histograms
2.1 Histograms
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Histogram Equalization: Histogram Equalization: ScalingScaling
2.1 Histograms
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Histogram EqualizationHistogram Equalization
2.1 Histograms
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P-Tile Method for ThresholdP-Tile Method for Threshold
2.2 Thresholding Techniques
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Region Segmentation for Region Segmentation for Multiple ObjectsMultiple Objects
2.2 Thresholding Techniques
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Problem with Single Problem with Single ThresholdThreshold
2.2 Thresholding Techniques
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Automatic Threshold MethodAutomatic Threshold Method
2.2 Thresholding Techniques
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Adaptive Thresholding by Adaptive Thresholding by RegionsRegions
2.2 Thresholding Techniques
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Rubber Sheet InspectionRubber Sheet Inspection
2.3 Example: Rubber Inspection
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Rubber: Multiple ThresholdRubber: Multiple Threshold
Input Image ofBackIlluminatedRubber Sheet
Preprocessing1: NoiseRemoval viaGaussian Blur
Preprocessing2: BoundaryErosion
Partition imageinto rectangularregions
AutomaticThresholding fromHistogram PerRegion
Threshold
BinaryImagePerRegion
AssembledBinaryImage ofDefects
2.3 Example: Rubber Inspection
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b.
c.
d.
e.
a.
b.
c.
d.
e.
a.
b.
Rubber: Example OutputRubber: Example Output
2.3 Example: Rubber Inspection
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Size Filter to Noisy Binary Size Filter to Noisy Binary ImageImage
2.4 Binary Image Features
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Computing Position Computing Position (Centroid)(Centroid)
2.4 Binary Image Features
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Iterative Thinning OperationsIterative Thinning Operations
2.4 Binary Image Features
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Expanding and ShrinkingExpanding and Shrinking
2.4 Binary Image Features
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Horizontal and Vertical Horizontal and Vertical ProjectionsProjections
2.4 Binary Image Features
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Projections for OCRProjections for OCR
2.4 Binary Image Features
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Image Convolution: FilteringImage Convolution: Filtering
3.0 Image Filtering, Correlation Operations
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Salt / Pepper and Gaussian Salt / Pepper and Gaussian NoiseNoise
3.2 Common Filters
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Applying the Mean FilterApplying the Mean Filter
3.2 Common Filtes
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Result of 3x3, 5x5, and 7x7 Result of 3x3, 5x5, and 7x7 mean filtermean filter
3.2 Common Filters
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Applying the Median FilterApplying the Median Filter
3.2 Common Filters
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The Gaussian Filter: The Gaussian Filter: Continuous Continuous
3.2 Common Filters
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Template MatchingTemplate Matching
Template matching is the sum squared difference between image and template
Very similar to Convolution Used for Recognition: OCR and Others
3.3 Template Matching
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A Discreet (Digital) Gaussian A Discreet (Digital) Gaussian FilterFilter
3.2 Common Filters
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Edges: First & Second Edges: First & Second DerivativesDerivatives
3.4 Edge Detection
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LOG (Laplacian of Gaussian) LOG (Laplacian of Gaussian) FilterFilter
3.4 Edge Detection
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Paper InspectionPaper Inspection
3.4 Example: Paper Inspection
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Paper Inspection: DefectsPaper Inspection: Defects
3.4 Example: Paper Inspection
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Texture Feature ExtractionTexture Feature Extraction
Texture: Statistical Distribution of Gray Co-Occurence Matrix captures distribution Texture Measures from Co-Occurence:
Entropy Energy Homogeneity
3.6 Texture Features
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Food Inspection: TextureFood Inspection: Texture
3.7 Example: Food Inspection
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