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Titlebook: Handbook of Mathematical Methods in Imaging; Otmar Scherzer Reference work 2015Latest edition Springer Science+Business Media New York 201

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發(fā)表于 2025-3-21 17:51:54 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Handbook of Mathematical Methods in Imaging
編輯Otmar Scherzer
視頻videohttp://file.papertrans.cn/422/421621/421621.mp4
概述Expanded and revised second edition with 10+ new entries and 200 illustrations.Comprehensive and rigorous treatment of mathematical methods in imaging.Available in both print and electronic form, with
圖書(shū)封面Titlebook: Handbook of Mathematical Methods in Imaging;  Otmar Scherzer Reference work 2015Latest edition Springer Science+Business Media New York 201
描述.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..This expanded and revised second edition contains updates to existing chapters and 16 additional entries on important mathematical methods such as graph cuts, morphology, discrete geometry, PDEs, conformal methods, to name a few. 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 200 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..
出版日期Reference work 2015Latest edition
關(guān)鍵詞Algorithmic Reconstruction; Mathematical Imaging and Vision; Support Vector Machines; Variation in Imag
版次2
doihttps://doi.org/10.1007/978-1-4939-0790-8
isbn_ebook978-1-4939-0790-8
copyrightSpringer Science+Business Media New York 2015
The information of publication is updating

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EM Algorithms from a Non-stochastic Perspectivenguage, relying on conditional expectations to obtain a method for estimating parameters in statistics, the essence of the EM algorithm is not stochastic. The conventional formulation of the EM algorithm given in many texts and papers on the subject is inadequate. A new formulation is given here based on the notion of acceptable data.
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Iterative Solution Methodsve algorithms such as Kaczmarz, expectation maximization, and Bregman iterations. Our intention here is to cite convergence results in the sense of regularization and to provide further references to the literature.
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Otmar ScherzerExpanded and revised second edition with 10+ new entries and 200 illustrations.Comprehensive and rigorous treatment of mathematical methods in imaging.Available in both print and electronic form, with
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