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Titlebook: Human Motion Sensing and Recognition; A Fuzzy Qualitative Honghai Liu,Zhaojie Ju,Mehdi Khoury Book 2017 Springer-Verlag GmbH Germany 2017

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發(fā)表于 2025-3-21 16:41:32 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Human Motion Sensing and Recognition
副標(biāo)題A Fuzzy Qualitative
編輯Honghai Liu,Zhaojie Ju,Mehdi Khoury
視頻videohttp://file.papertrans.cn/430/429322/429322.mp4
概述Reflects the state-of-the-art in human motion sensing and recognition.Presents a comprehensive introduction to methodologies for human hand motion analysis, human–computer/robot interaction, smart sur
叢書名稱Studies in Computational Intelligence
圖書封面Titlebook: Human Motion Sensing and Recognition; A Fuzzy Qualitative  Honghai Liu,Zhaojie Ju,Mehdi Khoury Book 2017 Springer-Verlag GmbH Germany 2017
描述.This book introduces readers to the latest exciting advances in human motion sensing and recognition, from the theoretical development of fuzzy approaches to their applications. The topics covered include human motion recognition in 2D and 3D, hand motion analysis with contact sensors, and vision-based view-invariant motion recognition, especially from the perspective of Fuzzy Qualitative techniques..With the rapid development of technologies in microelectronics, computers, networks, and robotics over the last decade, increasing attention has been focused on human motion sensing and recognition in many emerging and active disciplines where human motions need to be automatically tracked, analyzed or understood, such as smart surveillance, intelligent human-computer interaction, robot motion learning, and interactive gaming. Current challenges mainly stem from the dynamic environment, data multi-modality, uncertain sensory information, and real-time issues..These techniques are shown to effectively address the above challenges by bridging the gap between symbolic cognitive functions and numerical sensing & control tasks in intelligent systems. The book not only serves as a valuable
出版日期Book 2017
關(guān)鍵詞Computational Intelligence; Gesture Recognition; Hand Skill Transfer; Human Hand Modeling; Intelligent A
版次1
doihttps://doi.org/10.1007/978-3-662-53692-6
isbn_softcover978-3-662-57152-1
isbn_ebook978-3-662-53692-6Series ISSN 1860-949X Series E-ISSN 1860-9503
issn_series 1860-949X
copyrightSpringer-Verlag GmbH Germany 2017
The information of publication is updating

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A Unified Fuzzy Framework for Human Hand Motion Recognition,represent components of hand gestures with a fast convergence; FEC utilises the dependence structure among the finger joint angles to recognise the motion type. The proposed algorithms have been evaluated on a wide range of scenarios of human hand recognition: (a) datasets including 13 grasps and 10
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Study of Human Action Recognition Based on Improved Spatio-Temporal Features,uantified and selected and finally tested by using Support Vector Machine (SVM) and AdaBoost-SVM recognition algorithm on the public KTH dataset. The testing results showed that the recognition rate has been significantly improved. Meantime, the test results verified the proposed features can more a
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A View-Invariant Action Recognition Based on Multi-view Space Hidden Markov Models,trained by HMMs algorithm in each sub-view space. By computing the probabilities of the test sequence (i.e. observation sequence) for the given multi-view space HMMs, the similarity between the sub-view space and the test sequence viewpoint are analysed during the recognition process. Finally the ac
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ed for all pairs of lotteries of this kind if and only if the von Neumann-Morgenstern utility function is concave. Moreover, the EU model does not imply that a randomization of lotteries matters (for instance, according to the EU model, a lottery whose consequences are a randomization of the outcome
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