Feature extraction and image processing for computer vision
Feature Extraction for Image Processing and Computer Vision is an essential guide to the implementation of image processing and computer vision techniques, with tutorial introductions and sample code in MATLAB and Python. Algorithms are presented and fully explained to enable complete understanding...
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| Main Authors | , |
|---|---|
| Format | eBook Book |
| Language | English |
| Published |
London
Academic Press
2020
Elsevier Science & Technology |
| Edition | 4 |
| Subjects | |
| Online Access | Get full text |
| ISBN | 0128149760 9780128149768 |
| DOI | 10.1016/C2017-0-02153-5 |
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| Abstract | Feature Extraction for Image Processing and Computer Vision is an essential guide to the implementation of image processing and computer vision techniques, with tutorial introductions and sample code in MATLAB and Python. Algorithms are presented and fully explained to enable complete understanding of the methods and techniques demonstrated. As one reviewer noted, "The main strength of the proposed book is the link between theory and exemplar code of the algorithms." Essential background theory is carefully explained. This text gives students and researchers in image processing and computer vision a complete introduction to classic and state-of-the art methods in feature extraction together with practical guidance on their implementation. |
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| AbstractList | Feature Extraction for Image Processing and Computer Vision is an essential guide to the implementation of image processing and computer vision techniques, with tutorial introductions and sample code in MATLAB and Python. Algorithms are presented and fully explained to enable complete understanding of the methods and techniques demonstrated. As one reviewer noted, "The main strength of the proposed book is the link between theory and exemplar code of the algorithms." Essential background theory is carefully explained. This text gives students and researchers in image processing and computer vision a complete introduction to classic and state-of-the art methods in feature extraction together with practical guidance on their implementation. |
| Author | Aguado, Alberto S. Nixon, Mark S. |
| Author_xml | – sequence: 1 fullname: Nixon, Mark S. – sequence: 2 fullname: Aguado, Alberto S. |
| BackLink | https://cir.nii.ac.jp/crid/1130848326353378048$$DView record in CiNii |
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| DOI | 10.1016/C2017-0-02153-5 |
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| EISBN | 0128149779 9780128149775 |
| Edition | 4 Fourth edition. |
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| Notes | "Academic Press is an imprint of Elsevier"--T.p. verso Previous edition: 2012 Includes bibliographical references and index |
| OCLC | 1129196165 |
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| Snippet | Feature Extraction for Image Processing and Computer Vision is an essential guide to the implementation of image processing and computer vision techniques,... |
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| SubjectTerms | Computer vision Computer vision -- Mathematics Image processing Image processing -- Digital techniques Pattern recognition systems |
| TableOfContents | 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 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) 11.3.2 CIE RGB colour matching functions Front Cover -- Feature Extraction and Image Processing for Computer Vision -- Feature Extraction and Image Processing for Computer Vision -- Copyright -- Dedication -- Contents -- 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 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 |
| Title | Feature extraction and image processing for computer vision |
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