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Feature Extraction &
Image Processing for
Computer Vision
Third edition
Mark S. Nixon
Alberto S. Aguado
1 Ж
1
v ).
лГжЖ
№"ЧУИМ?
ELSEVIER
AMSTERDAM • BOSTON • HEIDELBERG • LONDON
NEW Y0RK
* 0 X F 0 R D ' PARIS • SAN DIEGO
SAN FRANCISCO • SINGAPORE • SYDNEY • TOKYO
Academic Press is an imprint of Elsevier
Contents
Preface
About the authors
xi
xvii
•
CHAPTER 1
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
CHAPTER 2
2.1
2.2
2.3
2.4
2.5
2.6
Introduction
Overview
Human and computer vision
The human vision system
1.3.1 The eye
1.3.2 The neural system
1.3.3 Processing
Computer vision systems
1.4.1 Cameras
1.4.2 Computer interfaces
1.4.3 Processing an image
Mathematical systems
1.5.1 Mathematical tools
1.5.2 Hello Matlab, hello images!
1.5.3 Hello Mathcad!
Associated literature
1.6.1 Journals, magazines, and conferences
1.6.2 Textbooks
1.6.3 The Web
Conclusions
References
1
1
2
4
5
8
9
12
12
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35
Images, Sampling, and Frequency
Domain Processing
Overview
Image formation
The Fourier transform
The sampling criterion
The discrete Fourier transform
2.5.1 ID transform
2.5.2 2D transform
Other properties of the Fourier transform
2.6.1 Shift invariance
2.6.2 Rotation
2.6.3 Frequency scaling
2.6.4 Superposition (linearity)
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vi
Contents
2.7
Transforms other than Fourier
2.7.1 Discrete cosine transform
2.7.2 Discrete Hartley transform
2.7.3 Introductory wavelets
2.7.4 Other transforms
2.8 Applications using frequency domain properties
2.9 Further reading
2.10 References
CHAPTER 3
3.1
3.2
3.3
3.4
3.5
3.6
3.7
3.8
CHAPTER 4
4.1
4.2
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68
70
71
78
78
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81
Basic Image Processing Operations
Overview
Histograms
Point operators
3.3.1 Basic point operations
3.3.2 Histogram normalization
3.3.3 Histogram equalization
3.3.4 Thresholding
Group operations
3.4.1 Template convolution
3.4.2 Averaging operator
3.4.3 On different template size
3.4.4 Gaussian averaging operator
3.4.5 More on averaging
Other statistical operators
3.5.1 Median filter
3.5.2 Mode filter
3.5.3 Anisotropic diffusion
3.5.4 Force field transform
3.5.5 Comparison of statistical operators
Mathematical morphology
3.6.1 Morphological operators
3.6.2 Gray-level morphology
3.6.3 Gray-level erosion and dilation
3.6.4 Minkowski operators
Further reading
References
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134
Low-Level Feature Extraction (including
edge detection)
Overview
Edge detection
4.2.1 First-order edge-detection operators
4.2.2 Second-order edge-detection operators
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138
140
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161
Contents
4.3
4.4
4.5
4.6
4.7
CHAPTER 5
4.2.3 Other edge-detection operators
4.2.4 Comparison of edge-detection operators
4.2.5 Further reading on edge detection
Phase congruency
Localized feature extraction
4.4.1 Detecting image curvature (corner extraction)
4.4.2 Modern approaches: region/patch analysis
Describing image motion
4.5.1 Area-based approach
4.5.2 Differential approach
4.5.3 Further reading on optical flow
Further reading
References
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171
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High-Level Feature Extraction: Fixed Shape
Matching
217
5.1 Overview
5.2 Thresholding and subtraction
5.3 Template matching
5.3.1 Definition
5.3.2 Fourier transform implementation
5.3.3 Discussion of template matching
5.4 Feature extraction by low-level features
5.4.1 Appearance-based approaches
5.4.2 Distribution-based descriptors
5.5 Hough transform
5.5.1 Overview
5.5.2 Lines
5.5.3 HT for circles
5.5.4 HT for ellipses
5.5.5 Parameter space decomposition
5.5.6 Generalized HT
5.5.7 Other extensions to the HT
5.6 Further reading
5.7 References
СНАPTER6
6.1
6.2
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High-Level Feature Extraction: Deformable
Shape Analysis
293
Overview
Deformable shape analysis
6.2.1 Deformable templates
6.2.2 Parts-based shape analysis
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297
VII
VIM
Contents
6.3
6.4
6.5
6.6
6.7
CHAPTER 7
7.1
7.2
7.3
7.4
7.5
CHAPTER 8
8.1
8.2
8.3
8.4
Active
6.3.1
6.3.2
6.3.3
6.3.4
6.3.5
6.3.6
contours (snakes)
Basics
The Greedy algorithm for snakes
Complete (Kass) snake implementation
Other snake approaches
Further snake developments
Geometric active contours (level-set-based
approaches)
Shape skeletonization
6.4.1 Distance transforms
6.4.2 Symmetry
Flexible shape models—active shape and active
appearance
Further reading
References
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301
308
313
314
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338
338
Object Description
Overview
Boundary descriptions
7.2.1 Boundary and region
7.2.2 Chain codes
7.2.3 Fourier descriptors
Region descriptors
7.3.1 Basic region descriptors
7.3.2 Moments
Further reading
References
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349
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395
Introduction to Texture Description,
Segmentation, and Classification
Overview
What is texture?
Texture description
8.3.1 Performance requirements
8.3.2 Structural approaches
8.3.3 Statistical approaches
8.3.4 Combination approaches
8.3.5 Local binary patterns
8.3.6 Other approaches
Classification
8.4.1 Distance measures
8.4.2 The /c-nearest neighbor rule
8.4.3 Other classification approaches
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399
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403
403
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406
409
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428
Contents
8.5
8.6
8.7
CHAPTER 9
9.1
9.2
9.3
9.4
9.5
9.6
CHAPTER 10
Segmentation
Further reading
References
429
431
432
Moving Object Detection and Description
435
Overview
Moving object detection
9.2.1 Basic approaches
9.2.2 Modeling and adapting to the (static) background
9.2.3 Background segmentation by thresholding
9.2.4 Problems and advances
Tracking moving features
9.3.1 Tracking moving objects
9.3.2 Tracking by local search
9.3.3 Problems in tracking
9.3.4 Approaches to tracking
9.3.5 Meanshift and Camshift
9.3.6 Recent approaches
Moving feature extraction and description
9.4.1 Moving (biological) shape analysis
9.4.2 Detecting moving shapes by shape matching
in image sequences
9.4.3 Moving shape description
Further reading
References
435
437
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Appendix 1: Camera Geometry Fundamentals
10.1 Image geometry
10.2 Perspective camera
10.3 Perspective camera model
10.3.1 Homogeneous coordinates and projective
geometry
10.3.2 Perspective camera model analysis
10.3.3 Parameters of the perspective camera model
10.4 Affine camera
10.4.1 Affine camera model
10.4.2 Affine camera model and the perspective
projection
10.4.3 Parameters of the affine camera model
10.5 Weak perspective model
10.6 Example of camera models
10.7 Discussion
10.8 References
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ix
X
Contents
CHAPTER 11 Appendix 2: Least Squares Analysis
11.1 The least squares criterion
11.2 Curve fitting by least squares
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519
521
CHAPTER 12
12.1
12.2
12.3
12.4
12.5
12.6
12.7
12.8
12.9
12.10
12.11
Appendix 3: Principal Components Analysis
Principal components analysis
Data
Covariance
Covariance matrix
Data transformation
Inverse transformation
Eigenproblem
Solving the eigenproblem
PCA method summary
Example
References
525
525
526
526
529
530
531
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533
533
534
540
CHAPTER 13
13.1
13.2
13.3
Appendix 4: Color Images
Color images
Tristimulus theory
Color models
13.3.1 The colorimetric equation
13.3.2 Luminosity function
13.3.3 Perception based color models: the CIE RGB
andCIEXYZ
13.3.4 Uniform color spaces: CIE LUV and CIE LAB
13.3.5 Additive and subtractive color models: RGB
andCMY
13.3.6 Luminance and chrominance color models:
YUV, YIQ, and YCbCr
13.3.7 Perceptual color models: HSV and HLS
13.3.8 More color models
References
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545
13.4
Index
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