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Article: Mixed-state auto-models and motion texture modeling

TitleMixed-state auto-models and motion texture modeling
Authors
KeywordsAuto-models
Dynamic textures
Gaussian models
Mixed states
Motion analysis
Issue Date2006
PublisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0924-9907
Citation
Journal Of Mathematical Imaging And Vision, 2006, v. 25 n. 3, p. 387-402 How to Cite?
AbstractIn image motion analysis as well as for several application fields like daily pluviometry data modeling, observations contain two components of different nature. A first part is made with discrete values accounting for some symbolic information and a second part records a continuous (real-valued) measurement. We call such type of observations "mixed-state observations". In this work we introduce a generalization of Besag's auto-models to deal with mixed-state observations at each site of a lattice. A careful construction as well as important properties of the model will be given. A special class of positive Gaussian mixed-state auto-models is proposed for the analysis of motion textures from video sequences. This model is first explored via simulations. We then apply it to real images of dynamic natural scenes. © 2006 Springer Science + Business Media, LLC.
Persistent Identifierhttp://hdl.handle.net/10722/132618
ISSN
2015 Impact Factor: 1.461
2015 SCImago Journal Rankings: 0.901
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorBouthemy, Pen_HK
dc.contributor.authorHardouin, Cen_HK
dc.contributor.authorPiriou, Gen_HK
dc.contributor.authorYao, Jen_HK
dc.date.accessioned2011-03-28T09:27:02Z-
dc.date.available2011-03-28T09:27:02Z-
dc.date.issued2006en_HK
dc.identifier.citationJournal Of Mathematical Imaging And Vision, 2006, v. 25 n. 3, p. 387-402en_HK
dc.identifier.issn0924-9907en_HK
dc.identifier.urihttp://hdl.handle.net/10722/132618-
dc.description.abstractIn image motion analysis as well as for several application fields like daily pluviometry data modeling, observations contain two components of different nature. A first part is made with discrete values accounting for some symbolic information and a second part records a continuous (real-valued) measurement. We call such type of observations "mixed-state observations". In this work we introduce a generalization of Besag's auto-models to deal with mixed-state observations at each site of a lattice. A careful construction as well as important properties of the model will be given. A special class of positive Gaussian mixed-state auto-models is proposed for the analysis of motion textures from video sequences. This model is first explored via simulations. We then apply it to real images of dynamic natural scenes. © 2006 Springer Science + Business Media, LLC.en_HK
dc.languageengen_US
dc.publisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0924-9907en_HK
dc.relation.ispartofJournal of Mathematical Imaging and Visionen_HK
dc.subjectAuto-modelsen_HK
dc.subjectDynamic texturesen_HK
dc.subjectGaussian modelsen_HK
dc.subjectMixed statesen_HK
dc.subjectMotion analysisen_HK
dc.titleMixed-state auto-models and motion texture modelingen_HK
dc.typeArticleen_HK
dc.identifier.emailYao, J: jeffyao@hku.hken_HK
dc.identifier.authorityYao, J=rp01473en_HK
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1007/s10851-006-7251-1en_HK
dc.identifier.scopuseid_2-s2.0-33750923344en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33750923344&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume25en_HK
dc.identifier.issue3en_HK
dc.identifier.spage387en_HK
dc.identifier.epage402en_HK
dc.identifier.isiWOS:000241890100008-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridBouthemy, P=7005146506en_HK
dc.identifier.scopusauthoridHardouin, C=15032906000en_HK
dc.identifier.scopusauthoridPiriou, G=22433503700en_HK
dc.identifier.scopusauthoridYao, J=7403503451en_HK

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