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Título : Computer Vision Using Local Binary Patterns Tipo de documento: documento electrónico Autores: Matti Pietikäinen ; SpringerLink (Online service) ; Abdenour Hadid ; Guoying Zhao ; Timo Ahonen Editorial: London : Springer London Fecha de publicación: 2011 Otro editor: Imprint: Springer Colección: Computational Imaging and Vision, ISSN 1381-6446 num. 40 Número de páginas: XVI, 212 p Il.: online resource ISBN/ISSN/DL: 978-0-85729-748-8 Idioma : Inglés (eng) Palabras clave: Mathematics Computer graphics Image processing Pattern recognition Biometrics (Biology) Mathematics, general Imaging, Vision, Recognition and Graphics Processing Vision Signal, Speech Clasificación: 51 Matemáticas Resumen: The recent emergence of Local Binary Patterns (LBP) has led to significant progress in applying texture methods to various computer vision problems and applications. The focus of this research has broadened from 2D textures to 3D textures and spatiotemporal (dynamic) textures. Also, where texture was once utilized for applications such as remote sensing, industrial inspection and biomedical image analysis, the introduction of LBP-based approaches have provided outstanding results in problems relating to face and activity analysis, with future scope for face and facial expression recognition, biometrics, visual surveillance and video analysis. Computer Vision Using Local Binary Patterns provides a detailed description of the LBP methods and their variants both in spatial and spatiotemporal domains. This comprehensive reference also provides an excellent overview as to how texture methods can be utilized for solving different kinds of computer vision and image analysis problems. Source codes of the basic LBP algorithms, demonstrations, some databases and a comprehensive LBP bibliography can be found from an accompanying web site. Topics include: - Local binary patterns and their variants in spatial and spatiotemporal domains - Texture classification and segmentation, description of interest regions - Applications in image retrieval and 3D recognition - Recognition and segmentation of dynamic textures - Background subtraction, recognition of actions - Face analysis using still images and image sequences, visual speech recognition - LBP in various applications Written by pioneers of LBP, this book is an essential resource for researchers, professional engineers and graduate students in computer vision, image analysis and pattern recognition. The book will also be of interest to all those who work with specific applications of machine vision Nota de contenido: Background -- Local binary patterns for still images -- Spatiotemporal LBP -- Texture classification and segmentation -- Description of interest regions -- Applications in image retrieval and 3D recognition -- Recognition and segmentation of dynamic textures -- Background subtraction -- Recognition of actions -- Face analysis using still images -- Face analysis using image sequences -- Visual recognition of spoken phrases -- LBP in different applications En línea: http://dx.doi.org/10.1007/978-0-85729-748-8 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33135 Computer Vision Using Local Binary Patterns [documento electrónico] / Matti Pietikäinen ; SpringerLink (Online service) ; Abdenour Hadid ; Guoying Zhao ; Timo Ahonen . - London : Springer London : Imprint: Springer, 2011 . - XVI, 212 p : online resource. - (Computational Imaging and Vision, ISSN 1381-6446; 40) .
ISBN : 978-0-85729-748-8
Idioma : Inglés (eng)
Palabras clave: Mathematics Computer graphics Image processing Pattern recognition Biometrics (Biology) Mathematics, general Imaging, Vision, Recognition and Graphics Processing Vision Signal, Speech Clasificación: 51 Matemáticas Resumen: The recent emergence of Local Binary Patterns (LBP) has led to significant progress in applying texture methods to various computer vision problems and applications. The focus of this research has broadened from 2D textures to 3D textures and spatiotemporal (dynamic) textures. Also, where texture was once utilized for applications such as remote sensing, industrial inspection and biomedical image analysis, the introduction of LBP-based approaches have provided outstanding results in problems relating to face and activity analysis, with future scope for face and facial expression recognition, biometrics, visual surveillance and video analysis. Computer Vision Using Local Binary Patterns provides a detailed description of the LBP methods and their variants both in spatial and spatiotemporal domains. This comprehensive reference also provides an excellent overview as to how texture methods can be utilized for solving different kinds of computer vision and image analysis problems. Source codes of the basic LBP algorithms, demonstrations, some databases and a comprehensive LBP bibliography can be found from an accompanying web site. Topics include: - Local binary patterns and their variants in spatial and spatiotemporal domains - Texture classification and segmentation, description of interest regions - Applications in image retrieval and 3D recognition - Recognition and segmentation of dynamic textures - Background subtraction, recognition of actions - Face analysis using still images and image sequences, visual speech recognition - LBP in various applications Written by pioneers of LBP, this book is an essential resource for researchers, professional engineers and graduate students in computer vision, image analysis and pattern recognition. The book will also be of interest to all those who work with specific applications of machine vision Nota de contenido: Background -- Local binary patterns for still images -- Spatiotemporal LBP -- Texture classification and segmentation -- Description of interest regions -- Applications in image retrieval and 3D recognition -- Recognition and segmentation of dynamic textures -- Background subtraction -- Recognition of actions -- Face analysis using still images -- Face analysis using image sequences -- Visual recognition of spoken phrases -- LBP in different applications En línea: http://dx.doi.org/10.1007/978-0-85729-748-8 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33135 Ejemplares
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Título : From Gestalt Theory to Image Analysis : A Probabilistic Approach Tipo de documento: documento electrónico Autores: Agnés Desolneux ; SpringerLink (Online service) ; Lionel Moisan ; Jean-Michel Morel Editorial: New York, NY : Springer New York Fecha de publicación: 2008 Colección: Interdisciplinary Applied Mathematics, ISSN 0939-6047 num. 34 Número de páginas: XII, 276 p. 130 illus Il.: online resource ISBN/ISSN/DL: 978-0-387-74378-3 Idioma : Inglés (eng) Palabras clave: Computer science Image processing Partial differential equations Applied mathematics Engineering Algorithms Mathematics Visualization Science Processing and Vision Differential Equations Signal, Speech Applications of Clasificación: 51 Matemáticas Resumen: This book introduces the reader to a recent theory in Computer Vision yielding elementary techniques to analyse digital images. These techniques are inspired from and are a mathematical formalization of the Gestalt theory. Gestalt theory, which had never been formalized is a rigorous realm of vision psychology developped between 1923 and 1975. From the mathematical viewpoint the closest field to it is stochastic geometry, involving basic probability and statistics, in the context of image analysis. The book is intended for a multidisciplinary audience of researchers and engineers. It is self contained in three aspects: mathematics, vision and algorithms, and requires only a background of elementary calculus and probability. A large number of illustrations, exercises and examples are included. The authors maintain a public software, MegaWave, containing implementations of most of the image analysis techniques developed in the book Nota de contenido: Gestalt Theory -- The Helmholtz Principle -- Estimating the Binomial Tail -- Alignments in Digital Images -- Maximal Meaningfulness and the Exclusion Principle -- Modes of a Histogram -- Vanishing Points -- Contrasted Boundaries -- Variational or Meaningful Boundaries? -- Clusters -- Binocular Grouping -- A Psychophysical Study of the Helmholtz Principle -- Back to the Gestalt Programme -- Other Theories, Discussion En línea: http://dx.doi.org/10.1007/978-0-387-74378-3 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34191 From Gestalt Theory to Image Analysis : A Probabilistic Approach [documento electrónico] / Agnés Desolneux ; SpringerLink (Online service) ; Lionel Moisan ; Jean-Michel Morel . - New York, NY : Springer New York, 2008 . - XII, 276 p. 130 illus : online resource. - (Interdisciplinary Applied Mathematics, ISSN 0939-6047; 34) .
ISBN : 978-0-387-74378-3
Idioma : Inglés (eng)
Palabras clave: Computer science Image processing Partial differential equations Applied mathematics Engineering Algorithms Mathematics Visualization Science Processing and Vision Differential Equations Signal, Speech Applications of Clasificación: 51 Matemáticas Resumen: This book introduces the reader to a recent theory in Computer Vision yielding elementary techniques to analyse digital images. These techniques are inspired from and are a mathematical formalization of the Gestalt theory. Gestalt theory, which had never been formalized is a rigorous realm of vision psychology developped between 1923 and 1975. From the mathematical viewpoint the closest field to it is stochastic geometry, involving basic probability and statistics, in the context of image analysis. The book is intended for a multidisciplinary audience of researchers and engineers. It is self contained in three aspects: mathematics, vision and algorithms, and requires only a background of elementary calculus and probability. A large number of illustrations, exercises and examples are included. The authors maintain a public software, MegaWave, containing implementations of most of the image analysis techniques developed in the book Nota de contenido: Gestalt Theory -- The Helmholtz Principle -- Estimating the Binomial Tail -- Alignments in Digital Images -- Maximal Meaningfulness and the Exclusion Principle -- Modes of a Histogram -- Vanishing Points -- Contrasted Boundaries -- Variational or Meaningful Boundaries? -- Clusters -- Binocular Grouping -- A Psychophysical Study of the Helmholtz Principle -- Back to the Gestalt Programme -- Other Theories, Discussion En línea: http://dx.doi.org/10.1007/978-0-387-74378-3 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34191 Ejemplares
Signatura Medio Ubicación Sub-localización Sección Estado ningún ejemplar Mathematical Methods for Signal and Image Analysis and Representation / SpringerLink (Online service) ; Luc Florack ; Remco Duits ; Geurt Jongbloed ; Marie-Colette van Lieshout ; Laurie Davies (2012)
Título : Mathematical Methods for Signal and Image Analysis and Representation Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Luc Florack ; Remco Duits ; Geurt Jongbloed ; Marie-Colette van Lieshout ; Laurie Davies Editorial: London : Springer London Fecha de publicación: 2012 Colección: Computational Imaging and Vision, ISSN 1381-6446 num. 41 Número de páginas: XII, 320 p Il.: online resource ISBN/ISSN/DL: 978-1-4471-2353-8 Idioma : Inglés (eng) Palabras clave: Mathematics Image processing Computer science mathematics Mathematics, general Processing and Vision Mathematical Applications in Science Clasificación: 51 Matemáticas Resumen: Mathematical Methods for Signal and Image Analysis and Representation presents the mathematical methodology for generic image analysis tasks. In the context of this book an image may be any m-dimensional empirical signal living on an n-dimensional smooth manifold (typically, but not necessarily, a subset of spacetime). The existing literature on image methodology is rather scattered and often limited to either a deterministic or a statistical point of view. In contrast, this book brings together these seemingly different points of view in order to stress their conceptual relations and formal analogies. Furthermore, it does not focus on specific applications, although some are detailed for the sake of illustration, but on the methodological frameworks on which such applications are built, making it an ideal companion for those seeking a rigorous methodological basis for specific algorithms as well as for those interested in the fundamental methodology per se. Covering many topics at the forefront of current research, including anisotropic diffusion filtering of tensor fields, this book will be of particular interest to graduate and postgraduate students and researchers in the fields of computer vision, medical imaging and visual perception Nota de contenido: A Short Introduction to Diffusion-like Methods -- Adaptive Filtering using Channel Representations -- 3D-Coherence-Enhancing Diffusion Filtering for Matrix Fields -- Structural Adaptive Smoothing: Principles and Applications in Imaging -- SPD Tensors Regularization via Iwasawa Decomposition -- Sparse Representation of Video Data by Adaptive Tetrahedralizations -- Continuous Diffusion Wavelet Transforms and Scale Space over Euclidean Spaces and Noncommutative Lie Groups -- Left Invariant Evolution Equations on Gabor Transforms -- Scale Space Representations Locally Adapted to the Geometry of Base and Target Manifold -- An A Priori Model of Line Propagation -- Local Statistics on Shape Diffeomorphisms using a Depth Potential Function -- Preserving Time Structures while Denoising a Dynamical Image -- Interacting Adaptive Filters for Multiple Objects Detection -- Visual Data Recognition and Modeling based on Local Markovian Models -- Locally Specified Polygonal Markov Fields for Image Segmentation -- Regularization with Approximated L2 Maximum Entropy Method. En línea: http://dx.doi.org/10.1007/978-1-4471-2353-8 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32719 Mathematical Methods for Signal and Image Analysis and Representation [documento electrónico] / SpringerLink (Online service) ; Luc Florack ; Remco Duits ; Geurt Jongbloed ; Marie-Colette van Lieshout ; Laurie Davies . - London : Springer London, 2012 . - XII, 320 p : online resource. - (Computational Imaging and Vision, ISSN 1381-6446; 41) .
ISBN : 978-1-4471-2353-8
Idioma : Inglés (eng)
Palabras clave: Mathematics Image processing Computer science mathematics Mathematics, general Processing and Vision Mathematical Applications in Science Clasificación: 51 Matemáticas Resumen: Mathematical Methods for Signal and Image Analysis and Representation presents the mathematical methodology for generic image analysis tasks. In the context of this book an image may be any m-dimensional empirical signal living on an n-dimensional smooth manifold (typically, but not necessarily, a subset of spacetime). The existing literature on image methodology is rather scattered and often limited to either a deterministic or a statistical point of view. In contrast, this book brings together these seemingly different points of view in order to stress their conceptual relations and formal analogies. Furthermore, it does not focus on specific applications, although some are detailed for the sake of illustration, but on the methodological frameworks on which such applications are built, making it an ideal companion for those seeking a rigorous methodological basis for specific algorithms as well as for those interested in the fundamental methodology per se. Covering many topics at the forefront of current research, including anisotropic diffusion filtering of tensor fields, this book will be of particular interest to graduate and postgraduate students and researchers in the fields of computer vision, medical imaging and visual perception Nota de contenido: A Short Introduction to Diffusion-like Methods -- Adaptive Filtering using Channel Representations -- 3D-Coherence-Enhancing Diffusion Filtering for Matrix Fields -- Structural Adaptive Smoothing: Principles and Applications in Imaging -- SPD Tensors Regularization via Iwasawa Decomposition -- Sparse Representation of Video Data by Adaptive Tetrahedralizations -- Continuous Diffusion Wavelet Transforms and Scale Space over Euclidean Spaces and Noncommutative Lie Groups -- Left Invariant Evolution Equations on Gabor Transforms -- Scale Space Representations Locally Adapted to the Geometry of Base and Target Manifold -- An A Priori Model of Line Propagation -- Local Statistics on Shape Diffeomorphisms using a Depth Potential Function -- Preserving Time Structures while Denoising a Dynamical Image -- Interacting Adaptive Filters for Multiple Objects Detection -- Visual Data Recognition and Modeling based on Local Markovian Models -- Locally Specified Polygonal Markov Fields for Image Segmentation -- Regularization with Approximated L2 Maximum Entropy Method. En línea: http://dx.doi.org/10.1007/978-1-4471-2353-8 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32719 Ejemplares
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Título : Mathematical Problems in Image Processing : Partial Differential Equations and the Calculus of Variations Tipo de documento: documento electrónico Autores: Gilles Aubert ; SpringerLink (Online service) ; Pierre Kornprobst Editorial: New York, NY : Springer New York Fecha de publicación: 2006 Colección: Applied Mathematical Sciences, ISSN 0066-5452 num. 147 Número de páginas: XXXI, 379 p Il.: online resource ISBN/ISSN/DL: 978-0-387-44588-5 Idioma : Inglés (eng) Palabras clave: Mathematics Image processing Mathematical analysis Analysis (Mathematics) Partial differential equations Optics Optoelectronics Plasmons (Physics) Applied mathematics Engineering Optics, Optoelectronics, Plasmonics and Optical Devices Appl.Mathematics/Computational Methods of Differential Equations Processing Computer Vision Signal, Speech Clasificación: 51 Matemáticas Resumen: Partial differential equations (PDEs) and variational methods were introduced into image processing about fifteen years ago. Since then, intensive research has been carried out. The goals of this book are to present a variety of image analysis applications, the precise mathematics involved and how to discretize them. Thus, this book is intended for two audiences. The first is the mathematical community by showing the contribution of mathematics to this domain. It is also the occasion to highlight some unsolved theoretical questions. The second is the computer vision community by presenting a clear, self-contained and global overview of the mathematics involved in image processing problems. This work will serve as a useful source of reference and inspiration for fellow researchers in Applied Mathematics and Computer Vision, as well as being a basis for advanced courses within these fields. During the four years since the publication of the first edition, there has been substantial progress in the range of image processing applications covered by the PDE framework. The main goals of the second edition are to update the first edition by giving a coherent account of some of the recent challenging applications, and to update the existing material. In addition, this book provides the reader with the opportunity to make his own simulations with a minimal effort. To this end, programming tools are made available, which will allow the reader to implement and test easily some classical approaches. Reviews of the earlier edition: "Mathematical Problems in Image Processing is a major, elegant, and unique contribution to the applied mathematics literature, oriented toward applications in image processing and computer vision.... Researchers and practitioners working in the field will benefit by adding this book to their personal collection. Students and instructors will benefit by using this book as a graduate course textbook." -- SIAM Review "The Mathematician -- and he doesn't need to be a 'die-hard' applied mathematician -- will love it because there are all these spectacular applications of nontrivial mathematical techniques and he can even find some open theoretical questions. The numerical analyst will discover many challenging problems and implementations. The image processor will be an eager reader because the book provides all the mathematical elements, including most of the proofs.... Both content and typography are a delight. I can recommend the book warmly for theoretical and applied researchers." -- Bulletin of the Belgian Mathematics Nota de contenido: Foreword -- Preface to the Second Edition -- Preface -- Guide to the Main Mathematical Concepts and their Application -- Notation and Symbols -- Introduction -- Mathematical Preliminaries -- Image Restoration -- The Segmentation Problem -- Other Challenging Applications -- A Introduction to Finite Difference Methods -- B Experiment Yourself!- References -- Index En línea: http://dx.doi.org/10.1007/978-0-387-44588-5 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34844 Mathematical Problems in Image Processing : Partial Differential Equations and the Calculus of Variations [documento electrónico] / Gilles Aubert ; SpringerLink (Online service) ; Pierre Kornprobst . - New York, NY : Springer New York, 2006 . - XXXI, 379 p : online resource. - (Applied Mathematical Sciences, ISSN 0066-5452; 147) .
ISBN : 978-0-387-44588-5
Idioma : Inglés (eng)
Palabras clave: Mathematics Image processing Mathematical analysis Analysis (Mathematics) Partial differential equations Optics Optoelectronics Plasmons (Physics) Applied mathematics Engineering Optics, Optoelectronics, Plasmonics and Optical Devices Appl.Mathematics/Computational Methods of Differential Equations Processing Computer Vision Signal, Speech Clasificación: 51 Matemáticas Resumen: Partial differential equations (PDEs) and variational methods were introduced into image processing about fifteen years ago. Since then, intensive research has been carried out. The goals of this book are to present a variety of image analysis applications, the precise mathematics involved and how to discretize them. Thus, this book is intended for two audiences. The first is the mathematical community by showing the contribution of mathematics to this domain. It is also the occasion to highlight some unsolved theoretical questions. The second is the computer vision community by presenting a clear, self-contained and global overview of the mathematics involved in image processing problems. This work will serve as a useful source of reference and inspiration for fellow researchers in Applied Mathematics and Computer Vision, as well as being a basis for advanced courses within these fields. During the four years since the publication of the first edition, there has been substantial progress in the range of image processing applications covered by the PDE framework. The main goals of the second edition are to update the first edition by giving a coherent account of some of the recent challenging applications, and to update the existing material. In addition, this book provides the reader with the opportunity to make his own simulations with a minimal effort. To this end, programming tools are made available, which will allow the reader to implement and test easily some classical approaches. Reviews of the earlier edition: "Mathematical Problems in Image Processing is a major, elegant, and unique contribution to the applied mathematics literature, oriented toward applications in image processing and computer vision.... Researchers and practitioners working in the field will benefit by adding this book to their personal collection. Students and instructors will benefit by using this book as a graduate course textbook." -- SIAM Review "The Mathematician -- and he doesn't need to be a 'die-hard' applied mathematician -- will love it because there are all these spectacular applications of nontrivial mathematical techniques and he can even find some open theoretical questions. The numerical analyst will discover many challenging problems and implementations. The image processor will be an eager reader because the book provides all the mathematical elements, including most of the proofs.... Both content and typography are a delight. I can recommend the book warmly for theoretical and applied researchers." -- Bulletin of the Belgian Mathematics Nota de contenido: Foreword -- Preface to the Second Edition -- Preface -- Guide to the Main Mathematical Concepts and their Application -- Notation and Symbols -- Introduction -- Mathematical Preliminaries -- Image Restoration -- The Segmentation Problem -- Other Challenging Applications -- A Introduction to Finite Difference Methods -- B Experiment Yourself!- References -- Index En línea: http://dx.doi.org/10.1007/978-0-387-44588-5 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34844 Ejemplares
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Título : Medial Representations : Mathematics, Algorithms and Applications Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Siddiqi, Kaleem ; Stephen M. Pizer Editorial: Dordrecht : Springer Netherlands Fecha de publicación: 2008 Colección: Computational Imaging and Vision, ISSN 1381-6446 num. 37 Número de páginas: XVII, 439 p Il.: online resource ISBN/ISSN/DL: 978-1-4020-8658-8 Idioma : Inglés (eng) Palabras clave: Mathematics Artificial intelligence Image processing Algorithms Computer mathematics Geometry Processing and Vision Intelligence (incl. Robotics) Computational Science Engineering Clasificación: 51 Matemáticas Resumen: The last half century has seen the development of many biological or physical theories that have explicitly or implicitly involved medial descriptions of objects and other spatial entities in our world. Simultaneously mathematicians have studied the properties of these skeletal descriptions of shape, and, stimulated by the many areas where medial models are useful, computer scientists and engineers have developed numerous algorithms for computing and using these models. We bring this knowledge and experience together into this book in order to make medial technology more widely understood and used. Edited by Prof. K. Siddiqi and Prof. S. Pizer, renowned experts in the field and authors of five of the chapters, this book consists of an introductory chapter, two chapters on the major mathematical results on medial representations, five chapters on algorithms for extracting medial models from boundary or binary image descriptions of objects, and three chapters on applications in image analysis and other areas of study and design. These chapters have been integrated and combined with a mathematics notation appendix and a detailed glossary, bibliography and index. This book will serve the science and engineering communities using medial models and will provide learning material for students entering this field Nota de contenido: Mathematics -- Local Forms and Transitions of the Medial Axis -- Geometry and Medial Structure -- Algorithms -- Skeletons via Shocks of Boundary Evolution -- Discrete Skeletons from Distance Transforms in 2D and 3D -- Voronoi Skeletons -- Voronoi Methods for 3D Medial Axis Approximation -- Synthesis, Deformation, and Statistics of 3D Objects via M-Reps -- Applications -- Statistical Applications with Deformable M-Reps -- 3D Model Retrieval Using Medial Surfaces -- From the Infinitely Large to the Infinitely Small En línea: http://dx.doi.org/10.1007/978-1-4020-8658-8 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34294 Medial Representations : Mathematics, Algorithms and Applications [documento electrónico] / SpringerLink (Online service) ; Siddiqi, Kaleem ; Stephen M. Pizer . - Dordrecht : Springer Netherlands, 2008 . - XVII, 439 p : online resource. - (Computational Imaging and Vision, ISSN 1381-6446; 37) .
ISBN : 978-1-4020-8658-8
Idioma : Inglés (eng)
Palabras clave: Mathematics Artificial intelligence Image processing Algorithms Computer mathematics Geometry Processing and Vision Intelligence (incl. Robotics) Computational Science Engineering Clasificación: 51 Matemáticas Resumen: The last half century has seen the development of many biological or physical theories that have explicitly or implicitly involved medial descriptions of objects and other spatial entities in our world. Simultaneously mathematicians have studied the properties of these skeletal descriptions of shape, and, stimulated by the many areas where medial models are useful, computer scientists and engineers have developed numerous algorithms for computing and using these models. We bring this knowledge and experience together into this book in order to make medial technology more widely understood and used. Edited by Prof. K. Siddiqi and Prof. S. Pizer, renowned experts in the field and authors of five of the chapters, this book consists of an introductory chapter, two chapters on the major mathematical results on medial representations, five chapters on algorithms for extracting medial models from boundary or binary image descriptions of objects, and three chapters on applications in image analysis and other areas of study and design. These chapters have been integrated and combined with a mathematics notation appendix and a detailed glossary, bibliography and index. This book will serve the science and engineering communities using medial models and will provide learning material for students entering this field Nota de contenido: Mathematics -- Local Forms and Transitions of the Medial Axis -- Geometry and Medial Structure -- Algorithms -- Skeletons via Shocks of Boundary Evolution -- Discrete Skeletons from Distance Transforms in 2D and 3D -- Voronoi Skeletons -- Voronoi Methods for 3D Medial Axis Approximation -- Synthesis, Deformation, and Statistics of 3D Objects via M-Reps -- Applications -- Statistical Applications with Deformable M-Reps -- 3D Model Retrieval Using Medial Surfaces -- From the Infinitely Large to the Infinitely Small En línea: http://dx.doi.org/10.1007/978-1-4020-8658-8 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34294 Ejemplares
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