Información del autor
Autor Scherzer, Otmar |
Documentos disponibles escritos por este autor (3)
Refinar búsquedaHandbook of Mathematical Methods in Imaging / SpringerLink (Online service) ; Scherzer, Otmar (2011)
Título : Handbook of Mathematical Methods in Imaging Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Scherzer, Otmar Editorial: New York, NY : Springer New York Fecha de publicación: 2011 Número de páginas: eReference Il.: online resource ISBN/ISSN/DL: 978-0-387-92920-0 Idioma : Inglés (eng) Palabras clave: Mathematics Radiology Image processing Applied mathematics Engineering Numerical analysis Applications of Processing and Computer Vision Signal, Speech Analysis Imaging / Clasificación: 51 Matemáticas Resumen: The Handbook of Mathematical Methods in Imaging provides a comprehensive treatment of the mathematical techniques used in imaging science. The material is grouped into two central themes, namely, Inverse Problems (Algorithmic Reconstruction) and Signal and Image Processing. Each section within the themes covers applications (modeling), mathematics, numerical methods (using a case example) and open questions. Written by experts in the area, the presentation is mathematically rigorous. The entries are cross-referenced for easy navigation through connected topics. Available in both print and electronic forms, the handbook is enhanced by more than 150 illustrations and an extended bibliography. It will benefit students, scientists and researchers in applied mathematics. Engineers and computer scientists working in imaging will also find this handbook useful Nota de contenido: Introduction -- Part 1: Inverse Problems -- Tomography -- MR DTI -- Hybrid Methods -- Nonlinear Inverse Problems -- EIT -- Scattering -- Sampling Methods -- Expansion Methods -- Regularization Methods for Ill-Posed Problems -- Iterative Solution Methods -- Wave Phenomena -- Seismic -- Radar -- Ultrasound -- Part 2: Signal and Image Processing -- Morphological Image Processing -- Learning, Classification, Data Mining -- Partial Differential Equations -- Variational Methods for Image Analysis -- Level Set Methods Including Fast Marching Methods -- Segmentation -- Registration, Optical Flow -- Duality and Convex Minimization -- Spline, Statistics -- Wavelets -- Fourier Analysis -- Compressed Sensing -- Geometry Processing -- Compression -- Computational Geometry -- Shape Spaces -- PDEs and Variational Methods on Manifold -- References -- Subject Index En línea: http://dx.doi.org/10.1007/978-0-387-92920-0 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33084 Handbook of Mathematical Methods in Imaging [documento electrónico] / SpringerLink (Online service) ; Scherzer, Otmar . - New York, NY : Springer New York, 2011 . - eReference : online resource.
ISBN : 978-0-387-92920-0
Idioma : Inglés (eng)
Palabras clave: Mathematics Radiology Image processing Applied mathematics Engineering Numerical analysis Applications of Processing and Computer Vision Signal, Speech Analysis Imaging / Clasificación: 51 Matemáticas Resumen: The Handbook of Mathematical Methods in Imaging provides a comprehensive treatment of the mathematical techniques used in imaging science. The material is grouped into two central themes, namely, Inverse Problems (Algorithmic Reconstruction) and Signal and Image Processing. Each section within the themes covers applications (modeling), mathematics, numerical methods (using a case example) and open questions. Written by experts in the area, the presentation is mathematically rigorous. The entries are cross-referenced for easy navigation through connected topics. Available in both print and electronic forms, the handbook is enhanced by more than 150 illustrations and an extended bibliography. It will benefit students, scientists and researchers in applied mathematics. Engineers and computer scientists working in imaging will also find this handbook useful Nota de contenido: Introduction -- Part 1: Inverse Problems -- Tomography -- MR DTI -- Hybrid Methods -- Nonlinear Inverse Problems -- EIT -- Scattering -- Sampling Methods -- Expansion Methods -- Regularization Methods for Ill-Posed Problems -- Iterative Solution Methods -- Wave Phenomena -- Seismic -- Radar -- Ultrasound -- Part 2: Signal and Image Processing -- Morphological Image Processing -- Learning, Classification, Data Mining -- Partial Differential Equations -- Variational Methods for Image Analysis -- Level Set Methods Including Fast Marching Methods -- Segmentation -- Registration, Optical Flow -- Duality and Convex Minimization -- Spline, Statistics -- Wavelets -- Fourier Analysis -- Compressed Sensing -- Geometry Processing -- Compression -- Computational Geometry -- Shape Spaces -- PDEs and Variational Methods on Manifold -- References -- Subject Index En línea: http://dx.doi.org/10.1007/978-0-387-92920-0 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33084 Ejemplares
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Título : Mathematical Models for Registration and Applications to Medical Imaging Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Scherzer, Otmar Editorial: Berlin, Heidelberg : Springer Berlin Heidelberg Fecha de publicación: 2006 Colección: Mathematics in Industry, ISSN 1612-3956 num. 10 Número de páginas: X, 191 p. 54 illus., 12 illus. in color Il.: online resource ISBN/ISSN/DL: 978-3-540-34767-5 Idioma : Inglés (eng) Palabras clave: Mathematics Radiology Computer graphics Mathematical models Modeling and Industrial Imaging, Vision, Pattern Recognition Graphics Imaging / Clasificación: 51 Matemáticas Resumen: Image registration is an emerging topic in image processing with many applications in medical imaging, picture and movie processing. The classical problem of image registration is concerned with ?nding an appropriate transformation between two data sets. This fuzzy de?nition of registration requires a mathematical modeling and in particular a mathematical speci?cation of the terms appropriate transformations and correlation between data sets. Depending on the type of application, typically Euler, rigid, plastic, elastic deformations are considered. The variety of similarity p measures ranges from a simpleL distance between the pixel values of the data to mutual information or entropy distances. This goal of this book is to highlight by some experts in industry and medicine relevant and emerging image registration applications and to show new emerging mathematical technologies in these areas. Currently, many registration application are solved based on variational prin- ple requiring sophisticated analysis, such as calculus of variations and the theory of partial differential equations, to name but a few. Due to the numerical compl- ity of registration problems ef?cient numerical realization are required. Concepts like multi-level solver for partial differential equations, non-convex optimization, and so on play an important role. Mathematical and numerical issues in the area of registration are discussed by some of the experts in this volume. Moreover, the importance of registration for industry and medical imaging is discussed from a medical doctor and from a manufacturer point of view Nota de contenido: Numerical Methods -- A Generalized Image Registration Framework using Incomplete Image Information – with Applications to Lesion Mapping -- Medical Image Registration and Interpolation by Optical Flow with Maximal Rigidity -- Registration of Histological Serial Sectionings -- Computational Methods for Nonlinear Image Registration -- A Survey on Variational Optic Flow Methods for Small Displacements -- Applications -- Fast Image Matching for Generation of Panorama Ultrasound -- Inpainting of Movies Using Optical Flow -- Medical Applications -- Multimodality Registration in Daily Clinical Practice -- Colour Images En línea: http://dx.doi.org/10.1007/978-3-540-34767-5 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34984 Mathematical Models for Registration and Applications to Medical Imaging [documento electrónico] / SpringerLink (Online service) ; Scherzer, Otmar . - Berlin, Heidelberg : Springer Berlin Heidelberg, 2006 . - X, 191 p. 54 illus., 12 illus. in color : online resource. - (Mathematics in Industry, ISSN 1612-3956; 10) .
ISBN : 978-3-540-34767-5
Idioma : Inglés (eng)
Palabras clave: Mathematics Radiology Computer graphics Mathematical models Modeling and Industrial Imaging, Vision, Pattern Recognition Graphics Imaging / Clasificación: 51 Matemáticas Resumen: Image registration is an emerging topic in image processing with many applications in medical imaging, picture and movie processing. The classical problem of image registration is concerned with ?nding an appropriate transformation between two data sets. This fuzzy de?nition of registration requires a mathematical modeling and in particular a mathematical speci?cation of the terms appropriate transformations and correlation between data sets. Depending on the type of application, typically Euler, rigid, plastic, elastic deformations are considered. The variety of similarity p measures ranges from a simpleL distance between the pixel values of the data to mutual information or entropy distances. This goal of this book is to highlight by some experts in industry and medicine relevant and emerging image registration applications and to show new emerging mathematical technologies in these areas. Currently, many registration application are solved based on variational prin- ple requiring sophisticated analysis, such as calculus of variations and the theory of partial differential equations, to name but a few. Due to the numerical compl- ity of registration problems ef?cient numerical realization are required. Concepts like multi-level solver for partial differential equations, non-convex optimization, and so on play an important role. Mathematical and numerical issues in the area of registration are discussed by some of the experts in this volume. Moreover, the importance of registration for industry and medical imaging is discussed from a medical doctor and from a manufacturer point of view Nota de contenido: Numerical Methods -- A Generalized Image Registration Framework using Incomplete Image Information – with Applications to Lesion Mapping -- Medical Image Registration and Interpolation by Optical Flow with Maximal Rigidity -- Registration of Histological Serial Sectionings -- Computational Methods for Nonlinear Image Registration -- A Survey on Variational Optic Flow Methods for Small Displacements -- Applications -- Fast Image Matching for Generation of Panorama Ultrasound -- Inpainting of Movies Using Optical Flow -- Medical Applications -- Multimodality Registration in Daily Clinical Practice -- Colour Images En línea: http://dx.doi.org/10.1007/978-3-540-34767-5 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34984 Ejemplares
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Título : Variational Methods in Imaging Tipo de documento: documento electrónico Autores: Scherzer, Otmar ; SpringerLink (Online service) ; Grasmair, Markus ; Grossauer, Harald ; Haltmeier, Markus ; Lenzen, Frank Editorial: New York, NY : Springer New York Fecha de publicación: 2009 Colección: Applied Mathematical Sciences, ISSN 0066-5452 num. 167 Número de páginas: XIV, 320 p Il.: online resource ISBN/ISSN/DL: 978-0-387-69277-7 Idioma : Inglés (eng) Palabras clave: Mathematics Radiology Image processing Numerical analysis Calculus of variations Variations and Optimal Control; Optimization Processing Computer Vision Signal, Speech Analysis Imaging / Clasificación: 51 Matemáticas Resumen: This book is devoted to the study of variational methods in imaging. The presentation is mathematically rigorous and covers a detailed treatment of the approach from an inverse problems point of view. Key Features: - Introduces variational methods with motivation from the deterministic, geometric, and stochastic point of view - Bridges the gap between regularization theory in image analysis and in inverse problems - Presents case examples in imaging to illustrate the use of variational methods e.g. denoising, thermoacoustics, computerized tomography - Discusses link between non-convex calculus of variations, morphological analysis, and level set methods - Analyses variational methods containing classical analysis of variational methods, modern analysis such as G-norm properties, and non-convex calculus of variations - Uses numerical examples to enhance the theory This book is geared towards graduate students and researchers in applied mathematics. It can serve as a main text for graduate courses in image processing and inverse problems or as a supplemental text for courses on regularization. Researchers and computer scientists in the area of imaging science will also find this book useful Nota de contenido: Fundamentals of Imaging -- Case Examples of Imaging -- Image and Noise Models -- Regularization -- Variational Regularization Methods for the Solution of Inverse Problems -- Convex Regularization Methods for Denoising -- Variational Calculus for Non-convex Regularization -- Semi-group Theory and Scale Spaces -- Inverse Scale Spaces -- Mathematical Foundations -- Functional Analysis -- Weakly Differentiable Functions -- Convex Analysis and Calculus of Variations En línea: http://dx.doi.org/10.1007/978-0-387-69277-7 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33844 Variational Methods in Imaging [documento electrónico] / Scherzer, Otmar ; SpringerLink (Online service) ; Grasmair, Markus ; Grossauer, Harald ; Haltmeier, Markus ; Lenzen, Frank . - New York, NY : Springer New York, 2009 . - XIV, 320 p : online resource. - (Applied Mathematical Sciences, ISSN 0066-5452; 167) .
ISBN : 978-0-387-69277-7
Idioma : Inglés (eng)
Palabras clave: Mathematics Radiology Image processing Numerical analysis Calculus of variations Variations and Optimal Control; Optimization Processing Computer Vision Signal, Speech Analysis Imaging / Clasificación: 51 Matemáticas Resumen: This book is devoted to the study of variational methods in imaging. The presentation is mathematically rigorous and covers a detailed treatment of the approach from an inverse problems point of view. Key Features: - Introduces variational methods with motivation from the deterministic, geometric, and stochastic point of view - Bridges the gap between regularization theory in image analysis and in inverse problems - Presents case examples in imaging to illustrate the use of variational methods e.g. denoising, thermoacoustics, computerized tomography - Discusses link between non-convex calculus of variations, morphological analysis, and level set methods - Analyses variational methods containing classical analysis of variational methods, modern analysis such as G-norm properties, and non-convex calculus of variations - Uses numerical examples to enhance the theory This book is geared towards graduate students and researchers in applied mathematics. It can serve as a main text for graduate courses in image processing and inverse problems or as a supplemental text for courses on regularization. Researchers and computer scientists in the area of imaging science will also find this book useful Nota de contenido: Fundamentals of Imaging -- Case Examples of Imaging -- Image and Noise Models -- Regularization -- Variational Regularization Methods for the Solution of Inverse Problems -- Convex Regularization Methods for Denoising -- Variational Calculus for Non-convex Regularization -- Semi-group Theory and Scale Spaces -- Inverse Scale Spaces -- Mathematical Foundations -- Functional Analysis -- Weakly Differentiable Functions -- Convex Analysis and Calculus of Variations En línea: http://dx.doi.org/10.1007/978-0-387-69277-7 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33844 Ejemplares
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