TY - GEN
T1 - Spatial segmentation of temporal texture using mixture linear models
AU - Cooper, Lee Alex Donald
AU - Liu, Jun
AU - Huang, Kun
PY - 2007/12/1
Y1 - 2007/12/1
N2 - In this paper we propose a novel approach for the spatial segmentation of video sequences containing multiple temporal textures. This work is based on the notion that a single temporal texture can be represented by a low-dimensional linear model. For scenes containing multiple temporal textures, e.g. trees swaying adjacent a flowing river, we extend the single linear model to a mixture of linear models and segment the scene by identifying subspaces within the data using robust generalized principal component analysis (GPCA). Computation is reduced to minutes in Matlab by first identifying models from a sampling of the sequence and using the derived models to segment the remaining data. The effectiveness of our method has been demonstrated in several examples including an application in biomedical image analysis.
AB - In this paper we propose a novel approach for the spatial segmentation of video sequences containing multiple temporal textures. This work is based on the notion that a single temporal texture can be represented by a low-dimensional linear model. For scenes containing multiple temporal textures, e.g. trees swaying adjacent a flowing river, we extend the single linear model to a mixture of linear models and segment the scene by identifying subspaces within the data using robust generalized principal component analysis (GPCA). Computation is reduced to minutes in Matlab by first identifying models from a sampling of the sequence and using the derived models to segment the remaining data. The effectiveness of our method has been demonstrated in several examples including an application in biomedical image analysis.
UR - http://www.scopus.com/inward/record.url?scp=49949100084&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=49949100084&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-70932-9_11
DO - 10.1007/978-3-540-70932-9_11
M3 - Conference contribution
AN - SCOPUS:49949100084
SN - 3540709312
SN - 9783540709312
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 142
EP - 150
BT - Dynamical Vision - ICCV 2005 and ECCV 2006 Workshops, WDV 2005 and WDV 2006, Beijing, China, October 21, 2005, Graz, Austria, May 13, 2006, Revised Papers
T2 - 2nd International Workshop on Dynamical Vision, WDV 2006 - 9th European Conference on Computer Vision,(ECCV 2006)
Y2 - 13 May 2006 through 13 May 2006
ER -