Feature extraction and image processing for computer vision

Feature Extraction and Image Processing for Computer Vision
Copyright
Dedication
Preface
	What is new in the fourth edition?
	Why did we write this book?
	The book and its support
	In gratitude
	Final message
1. Introduction
	1.1 Overview
	1.2 Human and computer vision
	1.3 The human vision system
		1.3.1 The eye
		1.3.2 The neural system
		1.3.3 Processing
	1.4 Computer vision systems
		1.4.1 Cameras
		1.4.2 Computer interfaces
	1.5 Processing images
		1.5.1 Processing
		1.5.2 Hello Python, hello images!
		1.5.3 Mathematical tools
		1.5.4 Hello Matlab
	1.6 Associated literature
		1.6.1 Journals, magazines and conferences
		1.6.2 Textbooks
		1.6.3 The web
	1.7 Conclusions
	References
2. Images, sampling and frequency domain processing
	2.1 Overview
	2.2 Image formation
	2.3 The Fourier Transform
	2.4 The sampling criterion
	2.5 The discrete Fourier Transform
		2.5.1 One-dimensional transform
		2.5.2 Two-dimensional transform
	2.6 Properties of the Fourier Transform
		2.6.1 Shift invariance
		2.6.2 Rotation
		2.6.3 Frequency scaling
		2.6.4 Superposition (linearity)
		2.6.5 The importance of phase
	2.7 Transforms other than Fourier
		2.7.1 Discrete cosine transform
		2.7.2 Discrete Hartley Transform
		2.7.3 Introductory wavelets
			2.7.3.1 Gabor Wavelet
			2.7.3.2 Haar Wavelet
		2.7.4 Other transforms
	2.8 Applications using frequency domain properties
	2.9 Further reading
	References
3. Image processing
	3.1 Overview
	3.2 Histograms
	3.3 Point operators
		3.3.1 Basic point operations
		3.3.2 Histogram normalisation
		3.3.3 Histogram equalisation
		3.3.4 Thresholding
	3.4 Group operations
		3.4.1 Template convolution
		3.4.2 Averaging operator
		3.4.3 On different template size
		3.4.4 Template convolution via the Fourier transform
		3.4.5 Gaussian averaging operator
		3.4.6 More on averaging
	3.5 Other image processing operators
		3.5.1 Median filter
		3.5.2 Mode filter
		3.5.3 Nonlocal means
		3.5.4 Bilateral filtering
		3.5.5 Anisotropic diffusion
		3.5.6 Comparison of smoothing operators
		3.5.7 Force field transform
		3.5.8 Image ray transform
	3.6 Mathematical morphology
		3.6.1 Morphological operators
		3.6.2 Grey level morphology
		3.6.3 Grey level erosion and dilation
		3.6.4 Minkowski operators
	3.7 Further reading
	References
4. Low-level feature extraction (including edge detection)
	4.1 Overview
	4.2 Edge detection
		4.2.1 First-order edge detection operators
			4.2.1.1 Basic operators
			4.2.1.2 Analysis of the basic operators
			4.2.1.3 Prewitt edge detection operator
			4.2.1.4 Sobel edge detection operator
			4.2.1.5 The Canny edge detector
		4.2.2 Second-order edge detection operators
			4.2.2.1 Motivation
			4.2.2.2 Basic operators: The Laplacian
			4.2.2.3 The Marr–Hildreth operator
		4.2.3 Other edge detection operators
		4.2.4 Comparison of edge detection operators
		4.2.5 Further reading on edge detection
	4.3 Phase congruency
	4.4 Localised feature extraction
		4.4.1 Detecting image curvature (corner extraction)
			4.4.1.1 Definition of curvature
			4.4.1.2 Computing differences in edge direction
			4.4.1.3 Measuring curvature by changes in intensity (differentiation)
			4.4.1.4 Moravec and Harris detectors
			4.4.1.5 Further reading on curvature
		4.4.2 Feature point detection; region/patch analysis
			4.4.2.1 Scale invariant feature transform
			4.4.2.2 Speeded up robust features
			4.4.2.3 FAST, ORB, FREAK, LOCKY and other keypoint detectors
			4.4.2.4 Other techniques and performance issues
		4.4.3 Saliency
			4.4.3.1 Basic saliency
			4.4.3.2 Context aware saliency
			4.4.3.3 Other saliency operators
	4.5 Describing image motion
		4.5.1 Area-based approach
		4.5.2 Differential approach
		4.5.3 Recent developments: deep flow, epic flow and extensions
		4.5.4 Analysis of optical flow
	4.6 Further reading
	References
2. Images, sampling and frequency domain processing
	2.1 Overview
	2.2 Image formation
	2.3 The Fourier Transform
	2.4 The sampling criterion
	2.5 The discrete Fourier Transform
		2.5.1 One-dimensional transform
		2.5.2 Two-dimensional transform
	2.6 Properties of the Fourier Transform
		2.6.1 Shift invariance
		2.6.2 Rotation
		2.6.3 Frequency scaling
		2.6.4 Superposition (linearity)
		2.6.5 The importance of phase
	2.7 Transforms other than Fourier
		2.7.1 Discrete cosine transform
		2.7.2 Discrete Hartley Transform
		2.7.3 Introductory wavelets
			2.7.3.1 Gabor Wavelet
			2.7.3.2 Haar Wavelet
		2.7.4 Other transforms
	2.8 Applications using frequency domain properties
	2.9 Further reading
	References
3. Image processing
	3.1 Overview
	3.2 Histograms
	3.3 Point operators
		3.3.1 Basic point operations
		3.3.2 Histogram normalisation
		3.3.3 Histogram equalisation
		3.3.4 Thresholding
	3.4 Group operations
		3.4.1 Template convolution
		3.4.2 Averaging operator
		3.4.3 On different template size
		3.4.4 Template convolution via the Fourier transform
		3.4.5 Gaussian averaging operator
		3.4.6 More on averaging
	3.5 Other image processing operators
		3.5.1 Median filter
		3.5.2 Mode filter
		3.5.3 Nonlocal means
		3.5.4 Bilateral filtering
		3.5.5 Anisotropic diffusion
		3.5.6 Comparison of smoothing operators
		3.5.7 Force field transform
		3.5.8 Image ray transform
	3.6 Mathematical morphology
		3.6.1 Morphological operators
		3.6.2 Grey level morphology
		3.6.3 Grey level erosion and dilation
		3.6.4 Minkowski operators
	3.7 Further reading
	References
4. Low-level feature extraction (including edge detection)
	4.1 Overview
	4.2 Edge detection
		4.2.1 First-order edge detection operators
			4.2.1.1 Basic operators
			4.2.1.2 Analysis of the basic operators
			4.2.1.3 Prewitt edge detection operator
			4.2.1.4 Sobel edge detection operator
			4.2.1.5 The Canny edge detector
		4.2.2 Second-order edge detection operators
			4.2.2.1 Motivation
			4.2.2.2 Basic operators: The Laplacian
			4.2.2.3 The Marr–Hildreth operator
		4.2.3 Other edge detection operators
		4.2.4 Comparison of edge detection operators
		4.2.5 Further reading on edge detection
	4.3 Phase congruency
	4.4 Localised feature extraction
		4.4.1 Detecting image curvature (corner extraction)
			4.4.1.1 Definition of curvature
			4.4.1.2 Computing differences in edge direction
			4.4.1.3 Measuring curvature by changes in intensity (differentiation)
			4.4.1.4 Moravec and Harris detectors
			4.4.1.5 Further reading on curvature
		4.4.2 Feature point detection; region/patch analysis
			4.4.2.1 Scale invariant feature transform
			4.4.2.2 Speeded up robust features
			4.4.2.3 FAST, ORB, FREAK, LOCKY and other keypoint detectors
			4.4.2.4 Other techniques and performance issues
		4.4.3 Saliency
			4.4.3.1 Basic saliency
			4.4.3.2 Context aware saliency
			4.4.3.3 Other saliency operators
	4.5 Describing image motion
		4.5.1 Area-based approach
		4.5.2 Differential approach
		4.5.3 Recent developments: deep flow, epic flow and extensions
		4.5.4 Analysis of optical flow
	4.6 Further reading
	References
5. High-level feature extraction: fixed shape matching
	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
			5.4.1.1 Object detection by templates
			5.4.1.2 Object detection by combinations of parts
		5.4.2 Distribution-based descriptors
			5.4.2.1 Description by interest points (SIFT, SURF, BRIEF)
			5.4.2.2 Characterising object appearance and shape
	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
			5.5.5.1 Parameter space reduction for lines
			5.5.5.2 Parameter space reduction for circles
			5.5.5.3 Parameter space reduction for ellipses
		5.5.6 Generalised Hough transform
			5.5.6.1 Formal definition of the GHT
			5.5.6.2 Polar definition
			5.5.6.3 The GHT technique
			5.5.6.4 Invariant GHT
		5.5.7 Other extensions to the HT
	5.6 Further reading
	References
6. High-level feature extraction: deformable shape analysis
	6.1 Overview
	6.2 Deformable shape analysis
		6.2.1 Deformable templates
		6.2.2 Parts-based shape analysis
	6.3 Active contours (snakes)
		6.3.1 Basics
		6.3.2 The Greedy Algorithm for snakes
		6.3.3 Complete (Kass) Snake implementation
		6.3.4 Other Snake approaches
		6.3.5 Further Snake developments
		6.3.6 Geometric active contours (Level Set-Based Approaches)
	6.4 Shape Skeletonisation
		6.4.1 Distance transforms
		6.4.2 Symmetry
	6.5 Flexible shape models – active shape and active appearance
	6.6 Further reading
	References
7. Object description
	7.1 Overview and invariance requirements
	7.2 Boundary descriptions
		7.2.1 Boundary and region
		7.2.2 Chain codes
		7.2.3 Fourier descriptors
			7.2.3.1 Basis of Fourier descriptors
			7.2.3.2 Fourier expansion
			7.2.3.3 Shift invariance
			7.2.3.4 Discrete computation
			7.2.3.5 Cumulative angular function
			7.2.3.6 Elliptic Fourier descriptors
			7.2.3.7 Invariance
	7.3 Region descriptors
		7.3.1 Basic region descriptors
		7.3.2 Moments
			7.3.2.1 Definition and properties
			7.3.2.2 Geometric moments
			7.3.2.3 Geometric complex moments and centralised moments
			7.3.2.4 Rotation and scale invariant moments
			7.3.2.5 Zernike moments
			7.3.2.6 Tchebichef moments
			7.3.2.7 Krawtchouk moments
			7.3.2.8 Other moments
	7.4 Further reading
	References
8. Region-based analysis
	8.1 Overview
	8.2 Region-based analysis
		8.2.1 Watershed transform
		8.2.2 Maximally stable extremal regions
		8.2.3 Superpixels
			8.2.3.1 Basic techniques and normalised cuts
			8.2.3.2 Simple linear iterative clustering
	8.3 Texture description and analysis
		8.3.1 What is texture?
		8.3.2 Performance requirements
		8.3.3 Structural approaches
		8.3.4 Statistical approaches
			8.3.4.1 Co-occurrence matrix
			8.3.4.2 Learning-based approaches
		8.3.5 Combination approaches
		8.3.6 Local binary patterns
		8.3.7 Other approaches
		8.3.8 Segmentation by texture
	8.4 Further reading
	References
9. Moving object detection and description
	9.1 Overview
	9.2 Moving object detection
		9.2.1 Basic approaches
			9.2.1.1 Detection by subtracting the background
			9.2.1.2 Improving quality by morphology
		9.2.2 Modelling and adapting to the (static) background
		9.2.3 Background segmentation by thresholding
		9.2.4 Problems and advances
	9.3 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
			9.3.5.1 Kernel-based density estimation
			9.3.5.2 MeanShift tracking
				9.3.5.2.1 Similarity function
				9.3.5.2.2 Kernel profiles and shadow kernels
				9.3.5.2.3 Gradient maximisation
			9.3.5.3 Camshift technique
		9.3.6 Other approaches
	9.4 Moving feature extraction and description
		9.4.1 Moving (biological) shape analysis
		9.4.2 Space–time interest points
		9.4.3 Detecting moving shapes by shape matching in image sequences
		9.4.4 Moving shape description
	9.5 Further reading
	References
10. Camera geometry fundamentals
	10.1 Overview
	10.2 Projective space
		10.2.1 Homogeneous co-ordinates and projective geometry
		10.2.2 Representation of a line, duality and ideal points
		10.2.3 Transformations in the projective space
		10.2.4 Computing a planar homography
	10.3 The perspective camera
		10.3.1 Perspective camera model
		10.3.2 Parameters of the perspective camera model
		10.3.3 Computing a projection from an image
	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 Discussion
	10.7 Further reading
	References
11. Colour images
	11.1 Overview
	11.2 Colour image theory
		11.2.1 Colour images
		11.2.2 Tristimulus theory
		11.2.3 The colourimetric equation
		11.2.4 Luminosity function
	11.3 Perception-based colour models: CIE RGB and CIE XYZ
		11.3.1 CIE RGB colour model: Wright–Guild data
		11.3.2 CIE RGB colour matching functions
		11.3.3 CIE RGB chromaticity diagram and chromaticity co-ordinates
		11.3.4 CIE XYZ colour model
		11.3.5 CIE XYZ colour matching functions
		11.3.6 XYZ chromaticity diagram
		11.3.7 Uniform colour spaces: CIE LUV and CIE LAB
	11.4 Additive and subtractive colour models
		11.4.1 RGB and CMY
		11.4.2 Transformation between RGB models
		11.4.3 Transformation between RGB and CMY models
	11.5 Luminance and chrominance colour models
		11.5.1 YUV, YIQ and YCbCr models
		11.5.2 Luminance and gamma correction
		11.5.3 Chrominance
		11.5.4 Transformations between YUV, YIQ and RGB colour models
		11.5.5 Colour model for component video: YPbPr
		11.5.6 Colour model for digital video: YCbCr
	11.6 Additive perceptual colour models
		11.6.1 The HSV and HLS colour models
		11.6.2 The hexagonal model: HSV
		11.6.3 The triangular model: HLS
		11.6.4 Transformation between HLS and RGB
	11.7 More colour models
	References
12. Distance, classification and learning
	12.1 Overview
	12.2 Basis of classification and learning
	12.3 Distance and classification
		12.3.1 Distance measures
			12.3.1.1 Manhattan and Euclidean Ln norms
			12.3.1.2 Mahalanobis, Bhattacharrya and Matusita
			12.3.1.3 Histogram intersection, Chi2 (χ2) and the Earth Mover\'s distance
		12.3.2 The k-nearest neighbour for classification
	12.4 Neural networks and Support Vector Machines
	12.5 Deep learning
		12.5.1 Basis of deep learning
		12.5.2 Major deep learning architectures
		12.5.3 Deep learning for feature extraction
		12.5.4 Deep learning performance evaluation
	12.6 Further reading
	References
Index
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	B
	C
	D
	E
	F
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	H
	I
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	M
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	Q
	R
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