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Titlebook: Cognitive Systems and Signal Processing; Third International Fuchun Sun,Huaping Liu,Dewen Hu Conference proceedings 2017 Springer Nature S

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發(fā)表于 2025-3-21 19:30:13 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Cognitive Systems and Signal Processing
副標(biāo)題Third International
編輯Fuchun Sun,Huaping Liu,Dewen Hu
視頻videohttp://file.papertrans.cn/230/229137/229137.mp4
概述Includes supplementary material:
叢書名稱Communications in Computer and Information Science
圖書封面Titlebook: Cognitive Systems and Signal Processing; Third International  Fuchun Sun,Huaping Liu,Dewen Hu Conference proceedings 2017 Springer Nature S
描述.This book constitutes the refereed proceedings of the Third?International Conference on Cognitive Systems and Signal?Processing, ICCSIP2016, held in Beijing, China, in December 2016..The 59 revised full papers presented were carefully reviewed and selected from 171 submissions. The papers are organized in topical sections on?Control and Decision;?Image and Video;?Machine Learning;?Robotics;?Cognitive System;?Cognitive Signal Processing..
出版日期Conference proceedings 2017
關(guān)鍵詞Cognitive system; Robot; Tactile sensing; Machine learning; Robotic perception; Pattern recognition; Extre
版次1
doihttps://doi.org/10.1007/978-981-10-5230-9
isbn_softcover978-981-10-5229-3
isbn_ebook978-981-10-5230-9Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightSpringer Nature Singapore Pte Ltd. 2017
The information of publication is updating

書目名稱Cognitive Systems and Signal Processing影響因子(影響力)




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Semantics, Pragmatics and Meaning Revisitedthis paper, we propose a new efficient binary descriptor (namely BAG) for RGB-D image representation by combining appearance and geometric cues. Experimental results show that the proposed BAG descriptor produces better feature matching performance with faster matching speed and less memory than the existing methods.
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Natural Language Processing Applicationsoothing to resolve the zero probability problem. Thirdly, the maximum filtering is used to optimize the probability distribution matrix for classification. Experiments on three face databases show that the proposed algorithm is effective and performs better than some state-of-the-art algorithms.
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https://doi.org/10.1007/978-0-387-72926-8raining data as a template, then improve the DTW algorithm to match the skeleton frame in the test data and in the training data, to estimate the percentage of the action’s completion. The improved DTW algorithm achieves state-of-the-art performances on our dataset, higher than 90%.
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Self-tuning Motion Model for Visual Trackingelative distance of the target positions in the last two frames. Our method has achieved excellent performance when experimenting on the sequences where the targets move unstably, abruptly or even when partial occlusion exists, and the method is particularly robust to the unsuitable initial motion model.
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Vojtěch Svátek,Jan Rauch,Martin Ralbovskyhout using any forms of recursive functions. Our method can largely improve the brightness and contrast of night images, besides, suppress noise. Then we implement on real experiment, and simulation experiment to test our algorithms. Both results show the advantages of proposed algorithm over contrast pair, Meylan and Retinex.
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