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Titlebook: Computer Vision in Human-Computer Interaction; ECCV 2004 Workshop o Nicu Sebe,Michael Lew,Thomas S. Huang Conference proceedings 2004 Sprin

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書目名稱Computer Vision in Human-Computer Interaction
副標(biāo)題ECCV 2004 Workshop o
編輯Nicu Sebe,Michael Lew,Thomas S. Huang
視頻videohttp://file.papertrans.cn/235/234089/234089.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Computer Vision in Human-Computer Interaction; ECCV 2004 Workshop o Nicu Sebe,Michael Lew,Thomas S. Huang Conference proceedings 2004 Sprin
描述.This book constitutes the refereed proceedings of the International Workshop on Human-Computer Interaction, HCI 2004, held at ECCV 2004 in Prague, Czech Republic in May 2004...The 19 revised full papers presented together with an introductory overview and an invited paper were carefully reviewed and selected from 45 submissions. The papers are organized in topical sections on human-robot interaction, gesture recognition and body tracking, systems, and face and head..
出版日期Conference proceedings 2004
關(guān)鍵詞Computer Vision; biometrics; body tracking; emotion detection; face recognition; facial feature analysis;
版次1
doihttps://doi.org/10.1007/b97917
isbn_softcover978-3-540-22012-1
isbn_ebook978-3-540-24837-8Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2004
The information of publication is updating

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Arm-Pointer: 3D Pointing Interface for Real-World Interactionuser’s shoulders and arms. Therefore, an easy-to-use real-world oriented interaction system is realized using the proposed method. We developed an algorithm which uses weighted voting for robust recognition. A prototype system using a stereo vision camera was developed and the real-time recognition
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The Design of Dynamic Data Structuresse where the test subjects are showing the natural facial expressions based upon their emotional state. Second, we evaluate the several promising machine learning algorithms for emotion detection which include techniques such as Bayesian Networks, SVMs, and Decision trees.
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Authentic Emotion Detection in Real-Time Videose where the test subjects are showing the natural facial expressions based upon their emotional state. Second, we evaluate the several promising machine learning algorithms for emotion detection which include techniques such as Bayesian Networks, SVMs, and Decision trees.
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