Human object inpainting using manifold learning-based posture sequence estimation

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 u...

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Veröffentlicht in:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 20(2011), 11 vom: 01. Nov., Seite 3124-35
1. Verfasser: Ling, Chih-Hung (VerfasserIn)
Weitere Verfasser: Liang, Yu-Ming, Lin, Chia-Wen, Chen, Yong-Sheng, Liao, Hong-Yuan Mark
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2011
Zugriff auf das übergeordnete Werk:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Schlagworte:Journal Article Research Support, Non-U.S. Gov't
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520 |a 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 
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700 1 |a Liang, Yu-Ming  |e verfasserin  |4 aut 
700 1 |a Lin, Chia-Wen  |e verfasserin  |4 aut 
700 1 |a Chen, Yong-Sheng  |e verfasserin  |4 aut 
700 1 |a Liao, Hong-Yuan Mark  |e verfasserin  |4 aut 
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