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Título : Mathematical Image Processing : University of Orléans, France, March 29th - April 1st, 2010 Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Maïtine Bergounioux Editorial: Berlin, Heidelberg : Springer Berlin Heidelberg Fecha de publicación: 2011 Colección: Springer Proceedings in Mathematics, ISSN 2190-5614 num. 5 Número de páginas: X, 198 p Il.: online resource ISBN/ISSN/DL: 978-3-642-19604-1 Idioma : Inglés (eng) Palabras clave: Mathematics Image processing Mathematical optimization Optimization Processing and Computer Vision Clasificación: 51 Matemáticas Resumen: The contributions appearing in this volume are a snapshot of the different topics that were discussed during the conference. They mainly concern, image reconstruction, texture extraction and image classification and involve a variety of different methods and applications. Therefore it was impossible to split the papers into generic groups which is why they are presented in alphabetic order. However they mainly concern : texture analysis (5 papers) with different techniques (variational analysis, wavelet and morphological component analysis, fractional Brownian fields), geometrical methods (2 papers ) for restoration and invariant feature detection, classification (with multifractal analysis), neurosciences imaging and analysis of Multi-Valued Images Nota de contenido: Function spaces vs. Scaling Functions: Tools for Image Classification. Stéphane Jaffard, Patrice Abry and Stéphane Roux -- A Second Order Model for 3D-texture Extraction. Maïtine Bergounioux and Minh Phuong Tran -- Analysis of Texture Anisotropy Through Extended Fractional Brownian Fields. Hermine Biermé and Frédéric J.P. Richard -- Image Reconstruction via Hypoelliptic Diffusion on the Bundle of Directions of the Plane. Ugo Boscain, Jean Duplaix, Jean-Paul Gauthier, Francesco Rossi -- Projective Invariant Features Detection and the Registration group. Françoise Dibos -- Morphological Component Analysis for Decomposing Dynamic Textures. Sloven Dubois, Renaud Péteri and Michel Ménard -- Texture Enhancing Based on Variational Image Decomposition. F. Frühauf, C. Pontow, O. Scherzer -- A Locally Anisotropic Model for Image Texture Extraction. Loïc Piffet -- A Neural Field Model for Motion Estimation. Émilien Tlapale, Pierre Kornprobst, Guillaume Masson and Olivier Faugeras -- Non-Local Regularization and Registration of Multi-Valued Images by PDE’s and VariationalMethods on Higher Dimensional Spaces. David Tschumperlé and Luc Brun En línea: http://dx.doi.org/10.1007/978-3-642-19604-1 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33417 Mathematical Image Processing : University of Orléans, France, March 29th - April 1st, 2010 [documento electrónico] / SpringerLink (Online service) ; Maïtine Bergounioux . - Berlin, Heidelberg : Springer Berlin Heidelberg, 2011 . - X, 198 p : online resource. - (Springer Proceedings in Mathematics, ISSN 2190-5614; 5) .
ISBN : 978-3-642-19604-1
Idioma : Inglés (eng)
Palabras clave: Mathematics Image processing Mathematical optimization Optimization Processing and Computer Vision Clasificación: 51 Matemáticas Resumen: The contributions appearing in this volume are a snapshot of the different topics that were discussed during the conference. They mainly concern, image reconstruction, texture extraction and image classification and involve a variety of different methods and applications. Therefore it was impossible to split the papers into generic groups which is why they are presented in alphabetic order. However they mainly concern : texture analysis (5 papers) with different techniques (variational analysis, wavelet and morphological component analysis, fractional Brownian fields), geometrical methods (2 papers ) for restoration and invariant feature detection, classification (with multifractal analysis), neurosciences imaging and analysis of Multi-Valued Images Nota de contenido: Function spaces vs. Scaling Functions: Tools for Image Classification. Stéphane Jaffard, Patrice Abry and Stéphane Roux -- A Second Order Model for 3D-texture Extraction. Maïtine Bergounioux and Minh Phuong Tran -- Analysis of Texture Anisotropy Through Extended Fractional Brownian Fields. Hermine Biermé and Frédéric J.P. Richard -- Image Reconstruction via Hypoelliptic Diffusion on the Bundle of Directions of the Plane. Ugo Boscain, Jean Duplaix, Jean-Paul Gauthier, Francesco Rossi -- Projective Invariant Features Detection and the Registration group. Françoise Dibos -- Morphological Component Analysis for Decomposing Dynamic Textures. Sloven Dubois, Renaud Péteri and Michel Ménard -- Texture Enhancing Based on Variational Image Decomposition. F. Frühauf, C. Pontow, O. Scherzer -- A Locally Anisotropic Model for Image Texture Extraction. Loïc Piffet -- A Neural Field Model for Motion Estimation. Émilien Tlapale, Pierre Kornprobst, Guillaume Masson and Olivier Faugeras -- Non-Local Regularization and Registration of Multi-Valued Images by PDE’s and VariationalMethods on Higher Dimensional Spaces. David Tschumperlé and Luc Brun En línea: http://dx.doi.org/10.1007/978-3-642-19604-1 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33417 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 : Sparse and Redundant Representations : From Theory to Applications in Signal and Image Processing Tipo de documento: documento electrónico Autores: Michael Elad ; SpringerLink (Online service) Editorial: New York, NY : Springer New York Fecha de publicación: 2010 Número de páginas: XX, 376 p Il.: online resource ISBN/ISSN/DL: 978-1-4419-7011-4 Idioma : Inglés (eng) Palabras clave: Mathematics Image processing Mathematical analysis Analysis (Mathematics) Approximation theory models optimization Modeling and Industrial Optimization Approximations Expansions Processing Computer Vision Signal, Speech Clasificación: 51 Matemáticas Resumen: The field of sparse and redundant representation modeling has gone through a major revolution in the past two decades. This started with a series of algorithms for approximating the sparsest solutions of linear systems of equations, later to be followed by surprising theoretical results that guarantee these algorithms’ performance. With these contributions in place, major barriers in making this model practical and applicable were removed, and sparsity and redundancy became central, leading to state-of-the-art results in various disciplines. One of the main beneficiaries of this progress is the field of image processing, where this model has been shown to lead to unprecedented performance in various applications. This book provides a comprehensive view of the topic of sparse and redundant representation modeling, and its use in signal and image processing. It offers a systematic and ordered exposure to the theoretical foundations of this data model, the numerical aspects of the involved algorithms, and the signal and image processing applications that benefit from these advancements. The book is well-written, presenting clearly the flow of the ideas that brought this field of research to its current achievements. It avoids a succession of theorems and proofs by providing an informal description of the analysis goals and building this way the path to the proofs. The applications described help the reader to better understand advanced and up-to-date concepts in signal and image processing. Written as a text-book for a graduate course for engineering students, this book can also be used as an easy entry point for readers interested in stepping into this field, and for others already active in this area that are interested in expanding their understanding and knowledge. The book is accompanied by a Matlab software package that reproduces most of the results demonstrated in the book. A link to the free software is available on springer.com Nota de contenido: Sparse and Redundant Representations – Theoretical and Numerical Foundations -- Prologue -- Uniqueness and Uncertainty -- Pursuit Algorithms – Practice -- Pursuit Algorithms – Guarantees -- From Exact to Approximate Solutions -- Iterative-Shrinkage Algorithms -- Towards Average PerformanceAnalysis -- The Dantzig-Selector Algorithm -- From Theory to Practice – Signal and Image Processing Applications -- Sparsity-Seeking Methods in Signal Processing -- Image Deblurring – A Case Study -- MAP versus MMSE Estimation -- The Quest for a Dictionary -- Image Compression – Facial Images -- Image Denoising -- Other Applications -- Epilogue En línea: http://dx.doi.org/10.1007/978-1-4419-7011-4 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33638 Sparse and Redundant Representations : From Theory to Applications in Signal and Image Processing [documento electrónico] / Michael Elad ; SpringerLink (Online service) . - New York, NY : Springer New York, 2010 . - XX, 376 p : online resource.
ISBN : 978-1-4419-7011-4
Idioma : Inglés (eng)
Palabras clave: Mathematics Image processing Mathematical analysis Analysis (Mathematics) Approximation theory models optimization Modeling and Industrial Optimization Approximations Expansions Processing Computer Vision Signal, Speech Clasificación: 51 Matemáticas Resumen: The field of sparse and redundant representation modeling has gone through a major revolution in the past two decades. This started with a series of algorithms for approximating the sparsest solutions of linear systems of equations, later to be followed by surprising theoretical results that guarantee these algorithms’ performance. With these contributions in place, major barriers in making this model practical and applicable were removed, and sparsity and redundancy became central, leading to state-of-the-art results in various disciplines. One of the main beneficiaries of this progress is the field of image processing, where this model has been shown to lead to unprecedented performance in various applications. This book provides a comprehensive view of the topic of sparse and redundant representation modeling, and its use in signal and image processing. It offers a systematic and ordered exposure to the theoretical foundations of this data model, the numerical aspects of the involved algorithms, and the signal and image processing applications that benefit from these advancements. The book is well-written, presenting clearly the flow of the ideas that brought this field of research to its current achievements. It avoids a succession of theorems and proofs by providing an informal description of the analysis goals and building this way the path to the proofs. The applications described help the reader to better understand advanced and up-to-date concepts in signal and image processing. Written as a text-book for a graduate course for engineering students, this book can also be used as an easy entry point for readers interested in stepping into this field, and for others already active in this area that are interested in expanding their understanding and knowledge. The book is accompanied by a Matlab software package that reproduces most of the results demonstrated in the book. A link to the free software is available on springer.com Nota de contenido: Sparse and Redundant Representations – Theoretical and Numerical Foundations -- Prologue -- Uniqueness and Uncertainty -- Pursuit Algorithms – Practice -- Pursuit Algorithms – Guarantees -- From Exact to Approximate Solutions -- Iterative-Shrinkage Algorithms -- Towards Average PerformanceAnalysis -- The Dantzig-Selector Algorithm -- From Theory to Practice – Signal and Image Processing Applications -- Sparsity-Seeking Methods in Signal Processing -- Image Deblurring – A Case Study -- MAP versus MMSE Estimation -- The Quest for a Dictionary -- Image Compression – Facial Images -- Image Denoising -- Other Applications -- Epilogue En línea: http://dx.doi.org/10.1007/978-1-4419-7011-4 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33638 Ejemplares
Signatura Medio Ubicación Sub-localización Sección Estado ningún ejemplar Visualization and Processing of Tensor Fields / SpringerLink (Online service) ; Joachim Weickert ; Hans Hagen (2006)
Título : Visualization and Processing of Tensor Fields Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Joachim Weickert ; Hans Hagen Editorial: Berlin, Heidelberg : Springer Berlin Heidelberg Fecha de publicación: 2006 Colección: Mathematics and Visualization, ISSN 1612-3786 Número de páginas: XV, 481 p Il.: online resource ISBN/ISSN/DL: 978-3-540-31272-7 Idioma : Inglés (eng) Palabras clave: Mathematics Radiology Computer graphics Image processing Mathematical analysis Analysis (Mathematics) Visualization Differential geometry Imaging / Imaging, Vision, Pattern Recognition and Graphics Processing Vision Geometry Clasificación: 51 Matemáticas Resumen: Matrix-valued data sets - so-called second order tensor fields - have gained significant importance in scientific visualization and image processing due to recent developments such as diffusion tensor imaging. This book is the first edited volume that presents the state-of-the-art in the visualization and processing of tensor fields. It contains some longer chapters dedicated to surveys and tutorials of specific topics, as well as a great deal of original work by leading experts that has not been published before. It serves as an overview for the inquiring scientist, as a basic foundation for developers and practitioners, and as as a textbook for specialized classes and seminars for graduate and doctoral students Nota de contenido: An Introduction to Tensors -- Feature Detection with Tensors -- Adaptive Structure Tensors and their Applications -- On the Concept of a Local Greyvalue Distribution and the Adaptive Estimation of a Structure Tensor -- Low-level Feature Detection Using the Boundary Tensor -- Diffusion Tensor Imaging -- An Introduction to Computational Diffusion MRI: the Diffusion Tensor and Beyond -- Random Noise in Diffusion Tensor Imaging, its Destructive Impact and Some Corrections -- An Introduction to Visualization of Diffusion Tensor Imaging and Its Applications -- Anatomy-Based Visualizations of Diffusion Tensor Images of Brain White Matter -- Variational Regularization of Multiple Diffusion Tensor Fields -- Higher Rank Tensors in Diffusion MRI -- Visualization of Tensor Fields -- Strategies for Direct Visualization of Second-Rank Tensor Fields -- Tensor Invariants and their Gradients -- Visualizing the Topology of Symmetric, Second-Order, Time-Varying Two-Dimensional Tensor Fields -- Degenerate 3D Tensors -- Locating Closed Hyperstreamlines in Second Order Tensor Fields -- Tensor Field Visualization Using a Metric Interpretation -- Tensor Field Transformations -- Symmetric Positive-Definite Matrices: From Geometry to Applications and Visualization -- Continuous Tensor Field Approximation of Diffusion Tensor MRI data -- Tensor Field Interpolation with PDEs -- Diffusion-Tensor Image Registration -- Image Processing Methods for Tensor Fields -- Tensor Median Filtering and M-Smoothing -- Mathematical Morphology on Tensor Data Using the Loewner Ordering -- A Local Structure Measure for Anisotropic Regularization of Tensor Fields -- Tensor Field Regularization using Normalized Convolution and Markov Random Fields in a Bayesian Framework -- PDEs for Tensor Image Processing En línea: http://dx.doi.org/10.1007/3-540-31272-2 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34936 Visualization and Processing of Tensor Fields [documento electrónico] / SpringerLink (Online service) ; Joachim Weickert ; Hans Hagen . - Berlin, Heidelberg : Springer Berlin Heidelberg, 2006 . - XV, 481 p : online resource. - (Mathematics and Visualization, ISSN 1612-3786) .
ISBN : 978-3-540-31272-7
Idioma : Inglés (eng)
Palabras clave: Mathematics Radiology Computer graphics Image processing Mathematical analysis Analysis (Mathematics) Visualization Differential geometry Imaging / Imaging, Vision, Pattern Recognition and Graphics Processing Vision Geometry Clasificación: 51 Matemáticas Resumen: Matrix-valued data sets - so-called second order tensor fields - have gained significant importance in scientific visualization and image processing due to recent developments such as diffusion tensor imaging. This book is the first edited volume that presents the state-of-the-art in the visualization and processing of tensor fields. It contains some longer chapters dedicated to surveys and tutorials of specific topics, as well as a great deal of original work by leading experts that has not been published before. It serves as an overview for the inquiring scientist, as a basic foundation for developers and practitioners, and as as a textbook for specialized classes and seminars for graduate and doctoral students Nota de contenido: An Introduction to Tensors -- Feature Detection with Tensors -- Adaptive Structure Tensors and their Applications -- On the Concept of a Local Greyvalue Distribution and the Adaptive Estimation of a Structure Tensor -- Low-level Feature Detection Using the Boundary Tensor -- Diffusion Tensor Imaging -- An Introduction to Computational Diffusion MRI: the Diffusion Tensor and Beyond -- Random Noise in Diffusion Tensor Imaging, its Destructive Impact and Some Corrections -- An Introduction to Visualization of Diffusion Tensor Imaging and Its Applications -- Anatomy-Based Visualizations of Diffusion Tensor Images of Brain White Matter -- Variational Regularization of Multiple Diffusion Tensor Fields -- Higher Rank Tensors in Diffusion MRI -- Visualization of Tensor Fields -- Strategies for Direct Visualization of Second-Rank Tensor Fields -- Tensor Invariants and their Gradients -- Visualizing the Topology of Symmetric, Second-Order, Time-Varying Two-Dimensional Tensor Fields -- Degenerate 3D Tensors -- Locating Closed Hyperstreamlines in Second Order Tensor Fields -- Tensor Field Visualization Using a Metric Interpretation -- Tensor Field Transformations -- Symmetric Positive-Definite Matrices: From Geometry to Applications and Visualization -- Continuous Tensor Field Approximation of Diffusion Tensor MRI data -- Tensor Field Interpolation with PDEs -- Diffusion-Tensor Image Registration -- Image Processing Methods for Tensor Fields -- Tensor Median Filtering and M-Smoothing -- Mathematical Morphology on Tensor Data Using the Loewner Ordering -- A Local Structure Measure for Anisotropic Regularization of Tensor Fields -- Tensor Field Regularization using Normalized Convolution and Markov Random Fields in a Bayesian Framework -- PDEs for Tensor Image Processing En línea: http://dx.doi.org/10.1007/3-540-31272-2 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34936 Ejemplares
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Título : Visualization and Processing of Tensor Fields : Advances and Perspectives Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; David H. Laidlaw ; Joachim Weickert Editorial: Berlin, Heidelberg : Springer Berlin Heidelberg Fecha de publicación: 2009 Colección: Mathematics and Visualization, ISSN 1612-3786 Número de páginas: XVII, 376 p Il.: online resource ISBN/ISSN/DL: 978-3-540-88378-4 Idioma : Inglés (eng) Palabras clave: Mathematics Computer science graphics Image processing mathematics Computational and Numerical Analysis of Computing Imaging, Vision, Pattern Recognition Graphics Processing Vision Clasificación: 51 Matemáticas Nota de contenido: Models for Diffusion MRI -- Modelling, Fitting and Sampling in Diffusion MRI -- Tensors, Polynomials and Models for Directional Data -- A Mixture of Wisharts (MOW) Model for Multifiber Reconstruction -- The Algebra of Fourth-Order Tensors with Application to Diffusion MRI -- Higher-Level Analysis of Diffusion Images -- Structure-Specific Statistical Mapping of White Matter Tracts -- Analysis of Distance/Similarity Measures for Diffusion Tensor Imaging -- Tensor Field Visualization -- Tensor Glyph Warping: Visualizing Metric Tensor Fields using Riemannian Exponential Maps -- Interactive Volume Rendering of Diffusion Tensor Data -- Dense Glyph Sampling for Visualization -- Tensor Field Analysis in the Physical Sciences -- The Role of Tensor Fields for Satellite Gravity Gradiometry -- Tensor Visualization and Defect Detection for Nematic Liquid Crystals using Shape Characteristics -- A Tensor Approach to Elastography Analysis and Visualization -- Tensor Image Structure Models -- A Higher-Order Structure Tensor -- Monogenic Curvature Tensor as Image Model -- Filtering with Tensors -- A General Structure Tensor Concept and Coherence-Enhancing Diffusion Filtering for Matrix Fields -- Coordinates-Based Diffusion Over the Space of Symmetric Positive-Definite Matrices -- Variational Methods for Denoising Matrix Fields -- An Operator Algebraic Inverse Scale Space Method for Symmetric Matrix Valued Images En línea: http://dx.doi.org/10.1007/978-3-540-88378-4 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34043 Visualization and Processing of Tensor Fields : Advances and Perspectives [documento electrónico] / SpringerLink (Online service) ; David H. Laidlaw ; Joachim Weickert . - Berlin, Heidelberg : Springer Berlin Heidelberg, 2009 . - XVII, 376 p : online resource. - (Mathematics and Visualization, ISSN 1612-3786) .
ISBN : 978-3-540-88378-4
Idioma : Inglés (eng)
Palabras clave: Mathematics Computer science graphics Image processing mathematics Computational and Numerical Analysis of Computing Imaging, Vision, Pattern Recognition Graphics Processing Vision Clasificación: 51 Matemáticas Nota de contenido: Models for Diffusion MRI -- Modelling, Fitting and Sampling in Diffusion MRI -- Tensors, Polynomials and Models for Directional Data -- A Mixture of Wisharts (MOW) Model for Multifiber Reconstruction -- The Algebra of Fourth-Order Tensors with Application to Diffusion MRI -- Higher-Level Analysis of Diffusion Images -- Structure-Specific Statistical Mapping of White Matter Tracts -- Analysis of Distance/Similarity Measures for Diffusion Tensor Imaging -- Tensor Field Visualization -- Tensor Glyph Warping: Visualizing Metric Tensor Fields using Riemannian Exponential Maps -- Interactive Volume Rendering of Diffusion Tensor Data -- Dense Glyph Sampling for Visualization -- Tensor Field Analysis in the Physical Sciences -- The Role of Tensor Fields for Satellite Gravity Gradiometry -- Tensor Visualization and Defect Detection for Nematic Liquid Crystals using Shape Characteristics -- A Tensor Approach to Elastography Analysis and Visualization -- Tensor Image Structure Models -- A Higher-Order Structure Tensor -- Monogenic Curvature Tensor as Image Model -- Filtering with Tensors -- A General Structure Tensor Concept and Coherence-Enhancing Diffusion Filtering for Matrix Fields -- Coordinates-Based Diffusion Over the Space of Symmetric Positive-Definite Matrices -- Variational Methods for Denoising Matrix Fields -- An Operator Algebraic Inverse Scale Space Method for Symmetric Matrix Valued Images En línea: http://dx.doi.org/10.1007/978-3-540-88378-4 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34043 Ejemplares
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