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Sensing Object Semantics for Interactive Multimedia Applications 實現互動多媒體應用之物件語意感知技術

Wen-Huang Cheng 鄭文 皇 助研究員 Assistant Research Fellow, Academia Sinica , Taiwan 中央研究院資訊科技創新研究中心.

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Sensing Object Semantics for Interactive Multimedia Applications 實現互動多媒體應用之物件語意感知技術

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  1. Wen-Huang Cheng 鄭文皇 助研究員Assistant Research Fellow, Academia Sinica, Taiwan 中央研究院資訊科技創新研究中心 個人簡歷: Wen-Huang Cheng is an Assistant Research Fellow at the Research Center for Information Technology Innovation (CITI), Academia Sinica, Taipei, Taiwan. He is the founding leader of Multimedia Computing Laboratory (MCLab) in CITI: http://mclab.citi.sinica.edu.tw/. His current research interest includes multimedia content analysis, computer vision, mobile multimedia applications, and human computer interaction. His research has received best paper awards from IEEE Consumer Electronics Society, Taipei Chapter, and IEEE Signal Processing Society, Taipei Chapter. He received the B.S. and M.S. degrees in computer science and information engineering from National Taiwan University, Taipei, Taiwan, in 2002 and 2004, respectively, where he received the Ph.D. degree from the Graduate Institute of Networking and Multimedia in 2008. From 2009 to 2010, he was a principal researcher at the MagicLabs, HTC Corporation, Taoyuan, Taiwan. He has been serving as the technical program committees of various conferences including ACM MM, CVPR, ECCV, and ICMR. Sensing Object Semantics for Interactive Multimedia Applications 實現互動多媒體應用之物件語意感知技術 演講摘要: For the effective development of interactive multimedia applications, one key technology is the multimedia content analysis, especially its achievable semantic level, i.e. the level of comprehension for a multimedia system on the multimedia content. However, since semantic objects in consumer photos and videos are usually captured in uncontrolled conditions, such as various viewpoints, positions, scales, and background clutter, in this talk, we will present sensing techniques for giving robust semantic object retrieval and recognition in real-world scenes. Two application scenarios will be showcased to demonstrate the effectiveness of the proposed sensing techniques. In particular, one application is mobile vision for locating the visual objects precisely and meanwhile achieving real-time performances. Another application is human action video search for users to create a search query by freely and continuously posing any number of actions in front of the Kinect sensor.

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