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Conference Paper: Temporal modeling of motion textures with mixed-sates Markov chains

TitleTemporal modeling of motion textures with mixed-sates Markov chains
Authors
KeywordsImage segmentation
Markov processes
Motion analysis
Stochastic fields
Issue Date2008
PublisherIEEE
Citation
Icassp, Ieee International Conference On Acoustics, Speech And Signal Processing - Proceedings, 2008, p. 881-884 How to Cite?
AbstractDynamic textures are time-varying visual patterns that exhibit certain spatio-temporal stationarity properties and are displayed mostly by natural scene elements. In this paper, we present new statistical models for the characterization of motion in this type of sequences. First we observe that motion measurements present values of two types: a discrete component at zero expressing the absence of motion and a continuous distribution for the rest of the motion values. Thus, we define random variables with mixed-states and propose to model a sequence of motion maps as a Markov chain, where the transition densities are mixed-state probability densities. Based on this approach, we propose a method for dynamic texture segmentation in real sequences showing the efficiency of the proposal in dynamic content analysis applications. ©2008 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/132609
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorCrivelli, Ten_HK
dc.contributor.authorFrías, BCen_HK
dc.contributor.authorBouthemy, Pen_HK
dc.contributor.authorYao, JFen_HK
dc.date.accessioned2011-03-28T09:26:59Z-
dc.date.available2011-03-28T09:26:59Z-
dc.date.issued2008en_HK
dc.identifier.citationIcassp, Ieee International Conference On Acoustics, Speech And Signal Processing - Proceedings, 2008, p. 881-884en_HK
dc.identifier.issn1520-6149en_HK
dc.identifier.urihttp://hdl.handle.net/10722/132609-
dc.description.abstractDynamic textures are time-varying visual patterns that exhibit certain spatio-temporal stationarity properties and are displayed mostly by natural scene elements. In this paper, we present new statistical models for the characterization of motion in this type of sequences. First we observe that motion measurements present values of two types: a discrete component at zero expressing the absence of motion and a continuous distribution for the rest of the motion values. Thus, we define random variables with mixed-states and propose to model a sequence of motion maps as a Markov chain, where the transition densities are mixed-state probability densities. Based on this approach, we propose a method for dynamic texture segmentation in real sequences showing the efficiency of the proposal in dynamic content analysis applications. ©2008 IEEE.en_HK
dc.languageengen_US
dc.publisherIEEEen_US
dc.relation.ispartofICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedingsen_HK
dc.subjectImage segmentationen_HK
dc.subjectMarkov processesen_HK
dc.subjectMotion analysisen_HK
dc.subjectStochastic fieldsen_HK
dc.titleTemporal modeling of motion textures with mixed-sates Markov chainsen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailYao, JF: jeffyao@hku.hken_HK
dc.identifier.authorityYao, JF=rp01473en_HK
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1109/ICASSP.2008.4517751en_HK
dc.identifier.scopuseid_2-s2.0-51449086396en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-51449086396&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage881en_HK
dc.identifier.epage884en_HK
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridCrivelli, T=25824832900en_HK
dc.identifier.scopusauthoridFrías, BC=7003558300en_HK
dc.identifier.scopusauthoridBouthemy, P=7005146506en_HK
dc.identifier.scopusauthoridYao, JF=7403503451en_HK

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