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Titlebook: Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging; Mathematical Imaging Ke Chen,Carola-Bibiane Sch?nlieb,Lauren

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書目名稱Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging
副標(biāo)題Mathematical Imaging
編輯Ke Chen,Carola-Bibiane Sch?nlieb,Laurent Younces
視頻videohttp://file.papertrans.cn/422/421625/421625.mp4
概述Topics written by leading Imaging and Vision researchers.Balanced coverage of theories and algorithms.Ready access to the state of arts in Imaging and Vision.Exposition of learning based methods for I
圖書封面Titlebook: Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging; Mathematical Imaging Ke Chen,Carola-Bibiane Sch?nlieb,Lauren
描述.This handbook gathers together the state of the art on?mathematical models?and algorithms for imaging and vision. Its emphasis lies on?rigorous mathematical?methods, which represent the optimal solutions to a class of imaging and vision problems, and on effective algorithms, which are necessary for the methods to be translated to practical use in various applications. Viewing discrete images as data sampled from functional surfaces enables the use of advanced tools from calculus, functions and calculus of variations, and?nonlinear?optimization, and provides the basis of high-resolution imaging through?geometry and?variational models. Besides,?optimization naturally connects traditional model-driven approaches to the emerging data-driven approaches of machine and deep learning.??No other framework can provide comparable accuracy and precision to imaging and vision...Written by leading researchers in imaging and?vision, the chapters in this handbook all start with gentle introductions, which make this work accessible to graduate students. For newcomers to the field, the book provides a comprehensive and fast-track introduction to the content, to save time and get on with tackling ne
出版日期Living reference work 20210th edition
關(guān)鍵詞Mathematical Imaging and Vision; Nonlinear optimization; Calculus of variation; Efficient algorithms; De
doihttps://doi.org/10.1007/978-3-030-03009-4
isbn_ebook978-3-030-03009-4
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Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging978-3-030-03009-4
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Convex Non-convex Variational Models,egy has been proposed, which allows the use of non-convex regularization while maintaining convexity of the total cost function. This work focuses on a general class of parameterized non-convex sparsity-inducing separable and non-separable regularizers and their associated CNC variational models. Co
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