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Titlebook: Optimization for Computer Vision; An Introduction to C Marco Alexander Treiber Book 2013 Springer-Verlag London 2013

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發(fā)表于 2025-3-21 18:58:13 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)Optimization for Computer Vision
副標(biāo)題An Introduction to C
編輯Marco Alexander Treiber
視頻videohttp://file.papertrans.cn/704/703238/703238.mp4
概述Presents a comprehensive overview of topics of relevance to computer vision-related optimization.Facilitates understanding by focusing on the fundamental concepts, and providing clearly written and ea
叢書(shū)名稱(chēng)Advances in Computer Vision and Pattern Recognition
圖書(shū)封面Titlebook: Optimization for Computer Vision; An Introduction to C Marco Alexander Treiber Book 2013 Springer-Verlag London 2013
描述This practical and authoritative text/reference presents a broad introduction to the optimization methods used specifically in computer vision. In order to facilitate understanding, the presentation of the methods is supplemented by simple flow charts, followed by pseudocode implementations that reveal deeper insights into their mode of operation. These discussions are further supported by examples taken from important applications in computer vision. Topics and features: provides a comprehensive overview of computer vision-related optimization; covers a range of techniques from classical iterative multidimensional optimization to cutting-edge topics of graph cuts and GPU-suited total variation-based optimization; describes in detail the optimization methods employed in computer vision applications; illuminates key concepts with clearly written and step-by-step explanations; presents detailed information on implementation, including pseudocode for most methods.
出版日期Book 2013
版次1
doihttps://doi.org/10.1007/978-1-4471-5283-5
isbn_softcover978-1-4471-7065-5
isbn_ebook978-1-4471-5283-5Series ISSN 2191-6586 Series E-ISSN 2191-6594
issn_series 2191-6586
copyrightSpringer-Verlag London 2013
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Continuous Optimization,irection are repeated iteratively until convergence. These methods can be categorized according to the extent of information about the derivatives of the objective function they utilize into zero-order, first-order, and second-order methods. Schemes for both steps of the general proceeding are treat
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Correspondence Problems,ons runs into difficulties in that cases, and consequently, methods being more robust to outliers are required. Examples of robust schemes are the random sample consensus (RANSAC) or methods transforming the problem into a graph representation, such as spectral graph matching or bipartite graph matc
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2191-6586 clearly written and step-by-step explanations; presents detailed information on implementation, including pseudocode for most methods.978-1-4471-7065-5978-1-4471-5283-5Series ISSN 2191-6586 Series E-ISSN 2191-6594
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Book 2013timization methods employed in computer vision applications; illuminates key concepts with clearly written and step-by-step explanations; presents detailed information on implementation, including pseudocode for most methods.
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MM (Hidden Markov Model) has been introduced to enhance recognition performance. In this scheme, performance depends on the feature elements extracted from each sign language motion. Feature elements of sign language motions and their unification are investigated, and the recognition performance is
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