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Titlebook: Mathematical Methods for Signal and Image Analysis and Representation; Luc Florack,Remco Duits,Laurie Davies Book 2012 Springer-Verlag Lon

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發(fā)表于 2025-3-21 17:32:39 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Mathematical Methods for Signal and Image Analysis and Representation
編輯Luc Florack,Remco Duits,Laurie Davies
視頻videohttp://file.papertrans.cn/627/626249/626249.mp4
概述Provides a unique compilation of mathematical methodologies, both statistical as well as deterministic, for a variety of applications in signal and image analysis, and offers insight by emphasizing co
叢書名稱Computational Imaging and Vision
圖書封面Titlebook: Mathematical Methods for Signal and Image Analysis and Representation;  Luc Florack,Remco Duits,Laurie Davies Book 2012 Springer-Verlag Lon
描述.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 fiel
出版日期Book 2012
關(guān)鍵詞computer vision; image analysis; signal processing; visual perception
版次1
doihttps://doi.org/10.1007/978-1-4471-2353-8
isbn_softcover978-1-4471-5890-5
isbn_ebook978-1-4471-2353-8Series ISSN 1381-6446
issn_series 1381-6446
copyrightSpringer-Verlag London Limited 2012
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Adaptive Filtering Using Channel Representations, relation to density estimation is introduced. The fast and accurate scheme of virtual shift decoding is introduced and applied in several variants of channel smoothing: . For each method, an algorithmic description and some examples of results are provided, together with discussions and references
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Left Invariant Evolution Equations on Gabor Transforms,ation and modulation invariant operator on the space of signals, the corresponding operator on the reproducing kernel space of Gabor transforms must be left invariant, i.e. it should commute with the left regular action of the reduced Heisenberg group ... By using the left invariant vector fields on
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Scale Space Representations Locally Adapted to the Geometry of Base and Target Manifold,in by considering the extension into a fibre bundle structure. We elaborate on aspects of parametrization and gauge, as these are important in practical applications. We subsequently scrutinize two examples that are of interest in bio-mathematical modeling, viz. scale space on the unit sphere, used
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An A Priori Model of Line Propagation,on three simple and generally applicable assumptions we introduce a stochastic line propagation model with its resulting Fokker-Planck equation and Green’s function. The line model implies a line diffusion scheme that is not simply another anisotropic diffusion of scalar-valued luminosity functions,
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