Series 
Computational imaging and vision, 13816446 ; v. 41 

Computational imaging and vision ;
v. 41.

Subject 
Mathematical models.


Image processing  Digital techniques  Mathematical models.


Signal processing  Mathematical models.


Mathematics.


Mathematics 

Computer vision 

Mathematics, general 

Image Processing and Computer Vision 

Mathematical Applications in Computer Science 
Alt Name 
Florack, Luc.

Description 
1 online resource (xii, 317 pages). 
Bibliography Note 
Includes bibliographical references (pages 291312) and index. 
Contents 
A Short Introduction to Diffusionlike Methods  Adaptive Filtering using Channel Representations  3DCoherenceEnhancing 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. 
Summary 
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 mdimensional empirical signal living on an ndimensional 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. 
ISBN 
9781447123538 (electronic bk.) 

1447123530 (electronic bk.) 

1447123522 (print) 

9781447123521 (print) 

9781447123521 
ISBN/ISSN 
10.1007/9781447123538 

9786613576729 
OCLC # 
773924800 
Additional Format 
Printed edition: 9781447123521 
