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dc.contributor.author
Crivelli, Tomás
dc.contributor.author
Bouthemy, Patrick
dc.contributor.author
Cernuschi Frias, Bruno
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Yao, Jian Feng
dc.contributor.other
Wang, Liang
dc.contributor.other
Zhao, Guoying
dc.contributor.other
Cheng, Li
dc.contributor.other
Pietikäinen, Matti
dc.date.available
2020-07-24T15:01:01Z
dc.date.issued
2011
dc.identifier.citation
Crivelli, Tomás; Bouthemy, Patrick; Cernuschi Frias, Bruno; Yao, Jian Feng; Mixed-state Markov models in image motion analysis; Springer Verlag Berlín; 2011; 77-115
dc.identifier.isbn
978-0-85729-056-4
dc.identifier.uri
http://hdl.handle.net/11336/110167
dc.description.abstract
When analyzing motion observations extracted from image sequences one notes that the histogram of the velocity magnitude at each pixel shows a large probability mass at zero velocity, while the rest of the motion values may be appropriately modeled with a continuous distribution. This suggests the introduction of mixed-state random variables that have probability mass concentrated in discrete states, while they have a probability density over a continuous range of values. In the first part of the chapter, we give a comprehensive description of the theory behind mixed-state statistical models, in particular the development of mixed-state Markov models that permits to take into account spatial and temporal interaction. The presentation generalizes the case of simultaneous modeling of continuous values and any type of discrete symbolic states. For the second part, we present the application of mixed-state models to motion texture analysis. Motion textures correspond to the instantaneous apparent motion maps extracted from dynamic textures. They depict mixed-state motion values with a discrete state at zero and a Gaussian distributionfor the rest. Mixed-state Markov random fields and mixed-state Markov chains are defined and applied to motion texture recognition and tracking.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Springer Verlag Berlín
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
RANDOM FIELD
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MARKOV RANDOM FIELD
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DISCRETE STATE
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GIBBS DISTRIBUTION
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LEIBLER DIVERGENCE
dc.subject.classification
Otras Ciencias de la Computación e Información
dc.subject.classification
Ciencias de la Computación e Información
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
Mixed-state Markov models in image motion analysis
dc.type
info:eu-repo/semantics/publishedVersion
dc.type
info:eu-repo/semantics/bookPart
dc.type
info:ar-repo/semantics/parte de libro
dc.date.updated
2020-05-19T19:44:19Z
dc.journal.pagination
77-115
dc.journal.pais
Reino Unido
dc.journal.ciudad
Londres
dc.description.fil
Fil: Crivelli, Tomás. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Electronica; Argentina
dc.description.fil
Fil: Bouthemy, Patrick. No especifíca;
dc.description.fil
Fil: Cernuschi Frias, Bruno. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Argentino de Matemática Alberto Calderón; Argentina
dc.description.fil
Fil: Yao, Jian Feng. No especifíca;
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/chapter/10.1007/978-0-85729-057-1_4
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://doi.org/10.1007/978-0-85729-057-1_4
dc.conicet.paginas
372
dc.source.titulo
Machine learning for vision-based motion analysis
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