File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Mixed-state causal modeling for statistical KL-based motion texture tracking

TitleMixed-state causal modeling for statistical KL-based motion texture tracking
Authors
KeywordsKullback-Leibler divergence
Mixed-state Markov models
Motion textures
Visual tracking
Issue Date2010
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/patrec
Citation
Pattern Recognition Letters, 2010, v. 31 n. 14, p. 2286-2294 How to Cite?
AbstractWe are interested in the modeling and tracking of dynamic or motion textures, which refer to dynamic contents that can be classified as a texture with motion (fire, smoke, crowd of people). Experimentally we observe that they depict motion maps with values of a mixed type: a discrete value at zero (absence of motion) and continuous non-null motion values. We thus introduce a temporal mixed-state Markov model for the characterization of motion textures from which a set of 13 parameters is extracted as the descriptive feature of the dynamic content. Then, a motion texture tracking strategy is proposed using the conditional Kullback-Leibler (KL) divergence between mixed-state probability densities, which allows us to estimate the position using a statistical matching approach. © 2010 Elsevier B.V. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/132602
ISSN
2015 Impact Factor: 1.586
2015 SCImago Journal Rankings: 1.225
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorCrivelli, Ten_HK
dc.contributor.authorCernuschiFrias, Ben_HK
dc.contributor.authorBouthemy, Pen_HK
dc.contributor.authorYao, JFen_HK
dc.date.accessioned2011-03-28T09:26:56Z-
dc.date.available2011-03-28T09:26:56Z-
dc.date.issued2010en_HK
dc.identifier.citationPattern Recognition Letters, 2010, v. 31 n. 14, p. 2286-2294en_HK
dc.identifier.issn0167-8655en_HK
dc.identifier.urihttp://hdl.handle.net/10722/132602-
dc.description.abstractWe are interested in the modeling and tracking of dynamic or motion textures, which refer to dynamic contents that can be classified as a texture with motion (fire, smoke, crowd of people). Experimentally we observe that they depict motion maps with values of a mixed type: a discrete value at zero (absence of motion) and continuous non-null motion values. We thus introduce a temporal mixed-state Markov model for the characterization of motion textures from which a set of 13 parameters is extracted as the descriptive feature of the dynamic content. Then, a motion texture tracking strategy is proposed using the conditional Kullback-Leibler (KL) divergence between mixed-state probability densities, which allows us to estimate the position using a statistical matching approach. © 2010 Elsevier B.V. All rights reserved.en_HK
dc.languageengen_US
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/patrecen_HK
dc.relation.ispartofPattern Recognition Lettersen_HK
dc.subjectKullback-Leibler divergenceen_HK
dc.subjectMixed-state Markov modelsen_HK
dc.subjectMotion texturesen_HK
dc.subjectVisual trackingen_HK
dc.titleMixed-state causal modeling for statistical KL-based motion texture trackingen_HK
dc.typeArticleen_HK
dc.identifier.emailYao, JF: jeffyao@hku.hken_HK
dc.identifier.authorityYao, JF=rp01473en_HK
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1016/j.patrec.2010.06.016en_HK
dc.identifier.scopuseid_2-s2.0-77957925185en_HK
dc.identifier.hkuros194269en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-77957925185&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume31en_HK
dc.identifier.issue14en_HK
dc.identifier.spage2286en_HK
dc.identifier.epage2294en_HK
dc.identifier.isiWOS:000282384500023-
dc.publisher.placeNetherlandsen_HK
dc.identifier.scopusauthoridCrivelli, T=25824832900en_HK
dc.identifier.scopusauthoridCernuschiFrias, B=7003558300en_HK
dc.identifier.scopusauthoridBouthemy, P=7005146506en_HK
dc.identifier.scopusauthoridYao, JF=7403503451en_HK
dc.identifier.citeulike7593441-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats