標題: Human Object Inpainting Using Manifold Learning-Based Posture Sequence Estimation
作者: Ling, Chih-Hung
Liang, Yu-Ming
Lin, Chia-Wen
Chen, Yong-Sheng
Liao, Hong-Yuan Mark
資訊工程學系
Department of Computer Science
關鍵字: Dimensionality reduction;isomap;manifold learning;object completion;video inpainting
公開日期: 1-十一月-2011
摘要: 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.
URI: http://dx.doi.org/10.1109/TIP.2011.2158228
http://hdl.handle.net/11536/14706
ISSN: 1057-7149
DOI: 10.1109/TIP.2011.2158228
期刊: IEEE TRANSACTIONS ON IMAGE PROCESSING
Volume: 20
Issue: 11
起始頁: 3124
結束頁: 3135
顯示於類別:期刊論文


文件中的檔案:

  1. 000296016200011.pdf