Silhouette-based human pose estimation using reversible jump Markov chain Monte Carlo

S. S. Huang; L. C. Fu; P. Y. Hsiao

doi:10.1049/el:20060044

Silhouette-based human pose estimation using reversible jump Markov chain Monte Carlo

S. S. Huang^*, L. C. Fu, P. Y. Hsiao

^*此作品的通信作者

研究成果: 期刊稿件 › 文章 › 同行評審

1 引文斯高帕斯（Scopus）

摘要

A novel approach for recovering the human body configuration based on the silhouette is presented. By considering pose inference as traversing the difference subspaces and using a data-driven mechanism, reversible jump Markov chain Monte Carlo (RJMCMC) can explore such solution space very efficiently. Experimental results are provided to demonstrate the efficiency and effectiveness of the proposed approach.

原文	英語
頁（從 - 到）	575-577
頁數	3
期刊	Electronics Letters
卷	42
發行號	10
DOIs	https://doi.org/10.1049/el:20060044
出版狀態	已出版 - 2006
對外發佈	是

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title = "Silhouette-based human pose estimation using reversible jump Markov chain Monte Carlo",

abstract = "A novel approach for recovering the human body configuration based on the silhouette is presented. By considering pose inference as traversing the difference subspaces and using a data-driven mechanism, reversible jump Markov chain Monte Carlo (RJMCMC) can explore such solution space very efficiently. Experimental results are provided to demonstrate the efficiency and effectiveness of the proposed approach.",

author = "Huang, {S. S.} and Fu, {L. C.} and Hsiao, {P. Y.}",

year = "2006",

doi = "10.1049/el:20060044",

language = "英语",

volume = "42",

pages = "575--577",

journal = "Electronics Letters",

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T1 - Silhouette-based human pose estimation using reversible jump Markov chain Monte Carlo

AU - Huang, S. S.

AU - Fu, L. C.

AU - Hsiao, P. Y.

PY - 2006

Y1 - 2006

N2 - A novel approach for recovering the human body configuration based on the silhouette is presented. By considering pose inference as traversing the difference subspaces and using a data-driven mechanism, reversible jump Markov chain Monte Carlo (RJMCMC) can explore such solution space very efficiently. Experimental results are provided to demonstrate the efficiency and effectiveness of the proposed approach.

AB - A novel approach for recovering the human body configuration based on the silhouette is presented. By considering pose inference as traversing the difference subspaces and using a data-driven mechanism, reversible jump Markov chain Monte Carlo (RJMCMC) can explore such solution space very efficiently. Experimental results are provided to demonstrate the efficiency and effectiveness of the proposed approach.

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U2 - 10.1049/el:20060044

DO - 10.1049/el:20060044

M3 - 文章

AN - SCOPUS:33646705652

SN - 0013-5194

VL - 42

SP - 575

EP - 577

JO - Electronics Letters

JF - Electronics Letters

IS - 10

ER -

Silhouette-based human pose estimation using reversible jump Markov chain Monte Carlo

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