Spatial segmentation of temporal texture using mixture linear models

Lee Alex Donald Cooper*, Jun Liu, Kun Huang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationDynamical Vision - ICCV 2005 and ECCV 2006 Workshops, WDV 2005 and WDV 2006, Beijing, China, October 21, 2005, Graz, Austria, May 13, 2006, Revised Papers
Pages142-150
Number of pages9
DOIs
StatePublished - Dec 1 2007
Event2nd International Workshop on Dynamical Vision, WDV 2006 - 9th European Conference on Computer Vision,(ECCV 2006) - Graz, Austria
Duration: May 13 2006May 13 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4358 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Workshop on Dynamical Vision, WDV 2006 - 9th European Conference on Computer Vision,(ECCV 2006)
CountryAustria
CityGraz
Period5/13/065/13/06

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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