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Titlebook: Computer Vision, Pattern Recognition, Image Processing, and Graphics; 6th National Confere Renu Rameshan,Chetan Arora,Sumantra Dutta Roy Co

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書(shū)目名稱(chēng)Computer Vision, Pattern Recognition, Image Processing, and Graphics
副標(biāo)題6th National Confere
編輯Renu Rameshan,Chetan Arora,Sumantra Dutta Roy
視頻videohttp://file.papertrans.cn/235/234321/234321.mp4
叢書(shū)名稱(chēng)Communications in Computer and Information Science
圖書(shū)封面Titlebook: Computer Vision, Pattern Recognition, Image Processing, and Graphics; 6th National Confere Renu Rameshan,Chetan Arora,Sumantra Dutta Roy Co
描述.This book constitutes the refereed proceedings of the 6th National Conference on Computer Vision, Pattern Recognition, Image Processing, and Graphics, NCVPRIPG 2017, held in Mandi, India, in December 2017.. .The 48 revised full papers presented in this volume were carefully reviewed and selected from 147 submissions. The papers are organized in topical sections on video processing; image and signal processing; segmentation, retrieval, captioning; pattern recognition applications..
出版日期Conference proceedings 2018
關(guān)鍵詞computer vision; pattern recognition; image processing; graphics; video processing; object recognition; de
版次1
doihttps://doi.org/10.1007/978-981-13-0020-2
isbn_softcover978-981-13-0019-6
isbn_ebook978-981-13-0020-2Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightSpringer Nature Singapore Pte Ltd. 2018
The information of publication is updating

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The Traditional Theory of Economic Policy,paper, we propose a frame-by-frame but computationally efficient approach for video object segmentation by clustering visually similar generic object segments throughout the video. Our algorithm segments object instances appearing in the video and then performs clustering in order to group visually
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https://doi.org/10.1007/978-981-19-7485-4al temporal features from the video, using an extension of the Convolutional Neural Networks (CNN) to 3D. A Recurrent Neural Network (RNN) is then trained to classify each sequence considering the temporal evolution of the learned features for each time step. Experimental results on the CMU MoCap, U
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https://doi.org/10.1007/978-981-19-7485-4in order to make them perceptible while making sure that the background noise is not amplified. We apply Eulerian motion magnification on only the salient area of each frame of the video. The salient object is processed independent of the rest of the image using alpha matting aided by scribbles. We
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Main Findings and Research Outlook, improve our proficiency, it is important that we get a feedback on our performances in terms of where we went wrong. In this paper, we propose a framework for analyzing and issuing reports of action segments that were missed or anomalously performed. This involves comparing the performed sequence w
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