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Titlebook: Optimization Techniques in Computer Vision; Ill-Posed Problems a Mongi A. Abidi,Andrei V. Gribok,Joonki Paik Book 2016 Springer Internation

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發(fā)表于 2025-3-21 19:33:04 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Optimization Techniques in Computer Vision
副標(biāo)題Ill-Posed Problems a
編輯Mongi A. Abidi,Andrei V. Gribok,Joonki Paik
視頻videohttp://file.papertrans.cn/704/703174/703174.mp4
概述Features a comprehensive description of regularization through optimization.Contains a large selection of data fusion algorithms.Includes chapters devoted to video compression and enhancement.Includes
叢書名稱Advances in Computer Vision and Pattern Recognition
圖書封面Titlebook: Optimization Techniques in Computer Vision; Ill-Posed Problems a Mongi A. Abidi,Andrei V. Gribok,Joonki Paik Book 2016 Springer Internation
描述This book presents practical optimization techniques used in image processing and computer vision problems. Ill-posed problems are introduced and used as examples to show how each type of problem is related to typical image processing and computer vision problems. Unconstrained optimization gives the best solution based on numerical minimization of a single, scalar-valued objective function or cost function. Unconstrained optimization problems have been intensively studied, and many algorithms and tools have been developed to solve them. Most practical optimization problems, however, arise with a set of constraints. Typical examples of constraints include: (i) pre-specified pixel intensity range, (ii) smoothness or correlation with neighboring information, (iii) existence on a certain contour of lines or curves, and (iv) given statistical or spectral characteristics of the solution. Regularized optimization is a special method used to solve a class of constrained optimization problems.The term regularization refers to the transformation of an objective function with constraints into a different objective function, automatically reflecting constraints in the unconstrained minimizati
出版日期Book 2016
關(guān)鍵詞regularization parameter selection; shape representation in image processing; image interpolation algo
版次1
doihttps://doi.org/10.1007/978-3-319-46364-3
isbn_softcover978-3-319-83501-3
isbn_ebook978-3-319-46364-3Series ISSN 2191-6586 Series E-ISSN 2191-6594
issn_series 2191-6586
copyrightSpringer International Publishing Switzerland 2016
The information of publication is updating

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發(fā)表于 2025-3-21 21:12:11 | 只看該作者
https://doi.org/10.1007/978-3-319-46364-3regularization parameter selection; shape representation in image processing; image interpolation algo
板凳
發(fā)表于 2025-3-22 00:59:00 | 只看該作者
Introduction to Optimizationematical models in order to cast the problems into optimization form. This chapter considers basic optimization theory and application as related to image processing. The focus presented in the examples will be to functions of one dimension, or line functions. Later chapters will give emphasis to the multidimensional case.
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發(fā)表于 2025-3-22 07:25:40 | 只看該作者
Frequency-Domain Implementation of RegularizationRegularization methods play an important role in solving linear equations of the form.with prior knowledge about the solution. The corresponding regularization results in minimization of
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Mongi A. Abidi,Andrei V. Gribok,Joonki Paikr akademische Vortrag, so sehr er die Hingabe des Einzelnen an ein begrenztes Gebiet des Wissens anregen und den Fortschritt der Wissenschaft f?rdern soll, kann als Lehrweise die Anstrebung der Nivellirung einer Schülerzahl nicht verleugnen. Für die Unterweisung im Heere, dessen Leistungen im Wesent
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Mongi A. Abidi,Andrei V. Gribok,Joonki Paikgen schon bei der Kondensation der Flüssigkeitsd?mpfe deren W?rme auf das Kühlwasser. Wir ?schlugen die D?mpfe nieder“, wir nahmen ihnen die W?rme, die sie aus Wasser zu D?mpfen gemacht hatte, und machten sie wieder zu Wasser. Das war ein W?rmeaustausch in unmittelbarer Berührung der D?mpfe mit dem
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