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Titlebook: Imaging, Vision and Learning Based on Optimization and PDEs; IVLOPDE, Bergen, Nor Xue-Cheng Tai,Egil Bae,Marius Lysaker Conference proceedi

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書目名稱Imaging, Vision and Learning Based on Optimization and PDEs
副標(biāo)題IVLOPDE, Bergen, Nor
編輯Xue-Cheng Tai,Egil Bae,Marius Lysaker
視頻videohttp://file.papertrans.cn/462/461823/461823.mp4
叢書名稱Mathematics and Visualization
圖書封面Titlebook: Imaging, Vision and Learning Based on Optimization and PDEs; IVLOPDE, Bergen, Nor Xue-Cheng Tai,Egil Bae,Marius Lysaker Conference proceedi
描述.This volume presents the peer-reviewed proceedings of the international conference Imaging, Vision and Learning Based on Optimization and PDEs (IVLOPDE), held in Bergen, Norway, in August/September 2016. The contributions cover state-of-the-art research on mathematical techniques for image processing, computer vision and machine learning based on optimization and partial differential equations (PDEs). .It has become an established paradigm to formulate problems within image processing and computer vision as PDEs, variational problems or finite dimensional optimization problems. This compact yet expressive framework makes it possible to incorporate a range of desired properties of the solutions and to design algorithms based on well-founded mathematical theory. A growing body of research has also approached more general problems within data analysis and machine learning from the same perspective, and demonstrated the advantages over earlier, more established algorithms. .This volume will appeal to all mathematicians and computer scientists interested in novel techniques and analytical results for optimization, variational models and PDEs, together with experimental results on appli
出版日期Conference proceedings 2018
關(guān)鍵詞image processing; computer vision; machine learning pattern recognition; optimization; partial different
版次1
doihttps://doi.org/10.1007/978-3-319-91274-5
isbn_ebook978-3-319-91274-5Series ISSN 1612-3786 Series E-ISSN 2197-666X
issn_series 1612-3786
copyrightSpringer International Publishing AG, part of Springer Nature 2018
The information of publication is updating

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On the Convex Model of Speckle Reductionrgence is analysed. The experimental results on some images subject to multiplicative noise as well as comparisons to other state-of-the-art methods are also presented. The results can verify that the new model is reasonable.
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