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    National Tsing Hua University Institutional Repository > 電機資訊學院 > 電機工程學系 > 期刊論文 >  Human object inpainting using manifold learning-based posture sequence estimation

    Please use this identifier to cite or link to this item: http://nthur.lib.nthu.edu.tw/dspace/handle/987654321/83633

    Title: Human object inpainting using manifold learning-based posture sequence estimation
    Authors: Chih-Hung Ling;Yu-Ming Liang;Chia-Wen Lin;Hong-Yuan Mark Liao;Yong-Sheng Chen
    教師: 林嘉文
    Date: 2011
    Publisher: Institute of Electrical and Electronics Engineers
    Relation: IEEE TRANSACTIONS ON IMAGE PROCESSING, Institute of Electrical and Electronics Engineers, Volume 20, Issue 11, NOV 2011, Pages 3124-3135
    Abstract: We propose a human object inpainting scheme that divides the process into three steps: 1) human posture synthesis; 2) graphical model construction; and 3) posture sequence estimation. Human posture synthesis is used to enrich the number of postures in the database, after which all the postures are used to build a graphical model that can estimate the motion tendency of an object. We also introduce two constraints to confine the motion continuity property. The first constraint limits the maximum search distance if a trajectory in the graphical model is discontinuous, and the second confines the search direction in order to maintain the tendency of an object's motion. We perform both forward and backward predictions to derive local optimal solutions. Then, to compute an overall best solution, we apply the Markov random field model and take the potential trajectory with the maximum total probability as the final result. The proposed posture sequence estimation model can help identify a set of suitable postures from the posture database to restore damaged/missing postures. It can also make a reconstructed motion sequence look continuous.
    Relation Link: http:/dx.doi.org/10.1109/TIP.2011.2158228
    URI: http://nthur.lib.nthu.edu.tw/dspace/handle/987654321/83633
    Appears in Collections:[電機工程學系] 期刊論文
    [光電研究中心] 期刊論文

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