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    National Tsing Hua University Institutional Repository > 電機資訊學院 > 電機工程學系 > 會議論文  >  Examplar-based object posture super-resolution using manifold learning


    Please use this identifier to cite or link to this item: http://nthur.lib.nthu.edu.tw/dspace/handle/987654321/83998


    Title: Examplar-based object posture super-resolution using manifold learning
    Authors: Chih-Hung Ling;Chia-Wen Lin;Chiou-Ting Hsu;Hong-Yuan Mark Liao
    教師: 林嘉文
    Date: 2012
    Publisher: Institute of Electrical and Electronics Engineers
    Relation: IEEE Workshop Multimedia Signal Processing (MMSP), Banff, Canada, 17-19 Sept. 2012
    Keywords: Exemplar-based
    super-resolution
    Abstract: This paper proposes a learning-based approach to increase the temporal resolutions of human motion sequences. Given a set of high resolution motion sequences, our idea is first to learn the motion tendency from this learning dataset and then synthesize new postures for the low-resolution sequence according to the learned motion tendency. We summarize the proposed framework in the following steps: (1) Each motion sequence is first projected into a low-dimension manifold space, where the local distance between postures could be better preserved. We then represent each of the projected motion sequences as a motion trajectory. (2) Next, motion priors learned from the HR training sequences are used to reconstruct the motion trajectory for the input sequence. (3) Finally, we use the reconstructed motion trajectory combined with object inpainting technique to generate the final result. Our experimental results demonstrate the effectiveness of the proposed method, and also show its outperformance over existing approaches.
    Relation Link: http://www.ieee.org/
    URI: http://nthur.lib.nthu.edu.tw/dspace/handle/987654321/83998
    Appears in Collections:[電機工程學系] 會議論文
    [光電研究中心] 會議論文

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