Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/41676
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dc.titleHuman posture analysis under partiel self-occlusion
dc.contributor.authorWang, R.
dc.contributor.authorLeow, W.K.
dc.date.accessioned2013-07-04T08:33:05Z
dc.date.available2013-07-04T08:33:05Z
dc.date.issued2006
dc.identifier.citationWang, R.,Leow, W.K. (2006). Human posture analysis under partiel self-occlusion. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4141 LNCS : 874-885. ScholarBank@NUS Repository.
dc.identifier.isbn3540448918
dc.identifier.issn03029743
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/41676
dc.description.abstractAccurate human posture estimation from single or multiple images is essential in many applications. Two main causes of difficulty to solve the estimation problem are large number of degrees of freedom and self-occlusion. Tree-structured graphical models with efficient inference algorithms have been used to solve the problem in a lower dimensional state space. However, such models are not accurate enough to formulate the problem because it assumes that the image of each body part can be independently observed. As a result, it is difficult to handle partial self-occlusion. This paper presents a more accurate graphical model which can implicitly model the possible self-occlusion between body parts. More important, an efficiently approximate inference algorithm is provided to estimate human posture in a low dimensional state space. It can deal with partial selfocclusion in posture estimation and human tracking, which has been shown by the experimental results on real data. © Springer-Verlag Berlin Heidelberg 2006.
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.description.volume4141 LNCS
dc.description.page874-885
dc.identifier.isiutNOT_IN_WOS
Appears in Collections:Staff Publications

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