Feature extraction & image processing for computer vision
Feature Extraction and Image Processing for Computer Vision is an essential guide to the implementation of image processing and computer vision techniques, with tutorial introductions and sample code in Matlab.Algorithms are presented and fully explained to enable complete understanding of the metho...
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| Médium: | E-kniha Kniha |
| Jazyk: | angličtina |
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Oxford
Academic Press
2012
Elsevier Science & Technology |
| Vydání: | 3 |
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| ISBN: | 0123965497, 9780123965493 |
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| Abstract | Feature Extraction and Image Processing for Computer Vision is an essential guide to the implementation of image processing and computer vision techniques, with tutorial introductions and sample code in Matlab.Algorithms are presented and fully explained to enable complete understanding of the methods and techniques demonstrated. |
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| AbstractList | Feature Extraction and Image Processing for Computer Vision is an essential guide to the implementation of image processing and computer vision techniques, with tutorial introductions and sample code in Matlab.Algorithms are presented and fully explained to enable complete understanding of the methods and techniques demonstrated. This book is an essential guide to the implementation of image processing and computer vision techniques, with tutorial introductions and sample code in Matlab. 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 exemplar code of the algorithms." Fully updated with the latest developments in feature extraction, including expanded tutorials and new techniques, this new edition contains extensive new material on Haar wavelets, Viola-Jones, bilateral filtering, SURF, PCA-SIFT, moving object detection and tracking, development of symmetry operators, LBP texture analysis, Adaboost, and a new appendix on color models. Coverage of distance measures, feature detectors, wavelets, level sets and texture tutorials has been extended. Named a 2012 Notable Computer Book for Computing Methodologies by Computing ReviewsEssential reading for engineers and students working in this cutting-edge fieldIdeal module text and background reference for courses in image processing and computer visionThe only currently available text to concentrate on feature extraction with working implementation and worked through derivation |
| Author | Aguado, Alberto S. Nixon, Mark S. |
| Author_xml | – sequence: 1 fullname: Nixon, Mark S. – sequence: 2 fullname: Aguado, Alberto S. |
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| Edition | 3 Third edition. |
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| Snippet | Feature Extraction and Image Processing for Computer Vision is an essential guide to the implementation of image processing and computer vision techniques,... This book is an essential guide to the implementation of image processing and computer vision techniques, with tutorial introductions and sample code in... |
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| SubjectTerms | Computer vision Computer vision -- Mathematics Digital techniques Image processing Image processing -- Digital techniques Mathematics Pattern recognition systems |
| TableOfContents | 3.4.5 More on averaging -- 3.5 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 -- 3.6 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 -- 3.7 Further reading -- 3.8 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 Localized 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 Modern approaches: region/patch analysis -- 4.4.2.1 Scale invariant feature transform -- 4.4.2.2 Speeded up robust features -- 4.4.2.3 Saliency -- 4.4.2.4 Other techniques and performance issues -- 4.5 Describing image motion -- 4.5.1 Area-based approach -- 4.5.2 Differential approach -- 4.5.3 Further reading on optical flow -- 4.6 Further reading -- 4.7 References -- 5 High-level feature extraction: fixed shape matching -- 5.1 Overview -- 5.2 Thresholding and subtraction 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 Basic properties -- 7.3.2.2 Invariant moments -- 7.3.2.3 Zernike moments -- 7.3.2.4 Other moments -- 7.4 Further reading -- 7.5 References -- 8 Introduction to texture description, segmentation, and classification -- 8.1 Overview -- 8.2 What is texture? -- 8.3 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 -- 8.4 Classification -- 8.4.1 Distance measures -- 8.4.2 The k-nearest neighbor rule -- 8.4.3 Other classification approaches -- 8.5 Segmentation -- 8.6 Further reading -- 8.7 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 Modeling 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 -- Similarity function -- Kernel profiles and shadow kernels -- Gradient maximization -- 9.3.5.3 Camshift technique -- 9.3.6 Recent approaches -- 9.4 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 -- 9.5 Further reading -- 9.6 References 13.3.6 Luminance and chrominance color models: YUV, YIQ, and YCbCr Front Cover -- Feature Extraction & -- Image Processing for Computer Vision -- Copyright page -- Contents -- Preface -- What is new in the third edition? -- Why did we write this book? -- The book and its support -- In gratitude -- Final message -- About the authors -- 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 -- 1.4.3 Processing an image -- 1.5 Mathematical systems -- 1.5.1 Mathematical tools -- 1.5.2 Hello Matlab, hello images! -- 1.5.3 Hello Mathcad! -- 1.6 Associated literature -- 1.6.1 Journals, magazines, and conferences -- 1.6.2 Textbooks -- 1.6.3 The Web -- 1.7 Conclusions -- 1.8 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 1D transform -- 2.5.2 2D transform -- 2.6 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) -- 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 -- 2.10 References -- 3 Basic image processing operations -- 3.1 Overview -- 3.2 Histograms -- 3.3 Point operators -- 3.3.1 Basic point operations -- 3.3.2 Histogram normalization -- 3.3.3 Histogram equalization -- 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 Gaussian averaging operator 10 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 -- 10.3.1.1 Representation of a line and duality -- 10.3.1.2 Ideal points -- 10.3.1.3 Transformations in the projective space -- 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 -- 11 Appendix 2: Least squares analysis -- 11.1 The least squares criterion -- 11.2 Curve fitting by least squares -- 12 Appendix 3: Principal components analysis -- 12.1 Principal components analysis -- 12.2 Data -- 12.3 Covariance -- 12.4 Covariance matrix -- 12.5 Data transformation -- 12.6 Inverse transformation -- 12.7 Eigenproblem -- 12.8 Solving the eigenproblem -- 12.9 PCA method summary -- 12.10 Example -- 12.11 References -- 13 Appendix 4: Color images -- 13.1 Color images -- 13.2 Tristimulus theory -- 13.3 Color models -- 13.3.1 The colorimetric equation -- 13.3.2 Luminosity function -- 13.3.3 Perception based color models: the CIE RGB and CIE XYZ -- 13.3.3.1 CIE RGB color model: Wright-Guild data -- 13.3.3.2 CIE RGB color matching functions -- 13.3.3.3 CIE RGB chromaticity diagram and chromaticity coordinates -- 13.3.3.4 CIE XYZ color model -- 13.3.3.5 CIE XYZ color matching functions -- 13.3.3.6 XYZ chromaticity diagram -- 13.3.4 Uniform color spaces: CIE LUV and CIE LAB -- 13.3.5 Additive and subtractive color models: RGB and CMY -- 13.3.5.1 RGB and CMY -- 13.3.5.2 Transformation between RGB color models -- 13.3.5.3 Transformation between RGB and CMY color models 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 -- 5.4.2.2 Characterizing 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 Generalized HT -- 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 -- 5.7 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 skeletonization -- 6.4.1 Distance transforms -- 6.4.2 Symmetry -- 6.5 Flexible shape models-active shape and active appearance -- 6.6 Further reading -- 6.7 References -- 7 Object description -- 7.1 Overview -- 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 |
| Title | Feature extraction & image processing for computer vision |
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