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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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🏛️國立陽明交通大學機構典藏

Author: search.filters.author.Liang, Yu-MingSubject: search.filters.subject.object completion

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    VIDEO OBJECT INPAINTING USING MANIFOLD-BASED ACTION PREDICTION
    (2010)
    Ling, Chih-Hung
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    Liang, Yu-Ming
    ;
    Lin, Chia-Wen
    ;
    Chen, Yong-Sheng
    ;
    Liao, Hong-Yuan Mark
    This paper presents a novel scheme for object completion in a video. The framework includes three steps: posture synthesis, graphical model construction, and action prediction. In the very beginning, a posture synthesis method is adopted to enrich the number of postures. Then, all postures are used to build a graphical model of object action which can provide possible motion tendency. We define two constraints to confine the motion continuity property. With the two constraints, possible candidates between every two consecutive postures are significantly reduced. Finally, we apply the Markov Random Field model to perform global matching. The proposed approach can effectively maintain the temporal continuity of the reconstructed motion. The advantage of this action prediction strategy is that it can handle the cases such as non-periodic motion or complete occlusion.
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    Human Object Inpainting Using Manifold Learning-Based Posture Sequence Estimation
    (2011-11-01)
    Ling, Chih-Hung
    ;
    Liang, Yu-Ming
    ;
    Lin, Chia-Wen
    ;
    Chen, Yong-Sheng
    ;
    Liao, Hong-Yuan Mark
    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.