000 04145nam a22005055i 4500
001 978-1-4471-2353-8
003 DE-He213
005 20140220083235.0
007 cr nn 008mamaa
008 120111s2012 xxk| s |||| 0|eng d
020 _a9781447123538
_9978-1-4471-2353-8
024 7 _a10.1007/978-1-4471-2353-8
_2doi
050 4 _aQA1-939
072 7 _aPB
_2bicssc
072 7 _aMAT000000
_2bisacsh
082 0 4 _a510
_223
100 1 _aFlorack, Luc.
_eeditor.
245 1 0 _aMathematical Methods for Signal and Image Analysis and Representation
_h[electronic resource] /
_cedited by Luc Florack, Remco Duits, Geurt Jongbloed, Marie-Colette Lieshout, Laurie Davies.
264 1 _aLondon :
_bSpringer London,
_c2012.
300 _aXII, 317p. 96 illus., 50 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aComputational Imaging and Vision,
_x1381-6446 ;
_v41
505 0 _aA 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.  .
520 _aMathematical 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.
650 0 _aMathematics.
650 0 _aComputer vision.
650 1 4 _aMathematics.
650 2 4 _aMathematics, general.
650 2 4 _aImage Processing and Computer Vision.
650 2 4 _aMathematical Applications in Computer Science.
700 1 _aDuits, Remco.
_eeditor.
700 1 _aJongbloed, Geurt.
_eeditor.
700 1 _aLieshout, Marie-Colette.
_eeditor.
700 1 _aDavies, Laurie.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781447123521
830 0 _aComputational Imaging and Vision,
_x1381-6446 ;
_v41
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4471-2353-8
912 _aZDB-2-SMA
999 _c100659
_d100659