3D human posture segmentation by spectral clustering with surface normal constraint

Cheng, J; Qiao, M; Bian, W; Tao, D

3D human posture segmentation by spectral clustering with surface normal constraint

Cheng, J Qiao, M

Bian, W Tao, D

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Publication Type:: Journal Article
Citation:: Signal Processing, 2011, 91 (9), pp. 2204 - 2212
Issue Date:: 2011-09-01

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Field	Value	Language
dc.contributor.author	Cheng, J	en_US
dc.contributor.author	Qiao, M https://orcid.org/0000-0002-0990-5506	en_US
dc.contributor.author	Bian, W	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.date.issued	2011-09-01	en_US
dc.identifier.citation	Signal Processing, 2011, 91 (9), pp. 2204 - 2212	en_US
dc.identifier.issn	0165-1684	en_US
dc.identifier.uri	http://hdl.handle.net/10453/15188
dc.description.abstract	In this paper, we propose a new algorithm for partitioning human posture represented by 3D point clouds sampled from the surface of human body. The algorithm is formed as a constrained extension of the recently developed segmentation method, spectral clustering (SC). Two folds of merits are offered by the algorithm: (1) as a nonlinear method, it is able to deal with the situation that data (point cloud) are sampled from a manifold (the surface of human body) rather than the embedded entire 3D space; (2) by using constraints, it facilitates the integration of multiple similarities for human posture partitioning, and it also helps to reduce the limitations of spectral clustering. We show that the constrained spectral clustering (CSC) still can be solved by generalized eigen-decomposition. Experimental results confirm the effectiveness of the proposed algorithm. © 2011 Elsevier B.V. All rights reserved.	en_US
dc.relation.ispartof	Signal Processing	en_US
dc.relation.isbasedon	10.1016/j.sigpro.2011.04.003	en_US
dc.subject.classification	Networking & Telecommunications	en_US
dc.title	3D human posture segmentation by spectral clustering with surface normal constraint	en_US
dc.type	Journal Article
utslib.citation.volume	9	en_US
utslib.citation.volume	91	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	10 Technology	en_US
dc.location.activity	WOS:000291772700005	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	open_access
pubs.issue	9	en_US
pubs.publication-status	Published	en_US
pubs.volume	91	en_US

Abstract:

In this paper, we propose a new algorithm for partitioning human posture represented by 3D point clouds sampled from the surface of human body. The algorithm is formed as a constrained extension of the recently developed segmentation method, spectral clustering (SC). Two folds of merits are offered by the algorithm: (1) as a nonlinear method, it is able to deal with the situation that data (point cloud) are sampled from a manifold (the surface of human body) rather than the embedded entire 3D space; (2) by using constraints, it facilitates the integration of multiple similarities for human posture partitioning, and it also helps to reduce the limitations of spectral clustering. We show that the constrained spectral clustering (CSC) still can be solved by generalized eigen-decomposition. Experimental results confirm the effectiveness of the proposed algorithm. © 2011 Elsevier B.V. All rights reserved.

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http://hdl.handle.net/10453/15188