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dc.contributor.author
Crivelli, Tomás
dc.contributor.author
Bouthemy, Patrick
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Cernuschi Frias, Bruno
dc.contributor.author
Yao, Jian-Feng
dc.date.available
2017-07-12T15:58:12Z
dc.date.issued
2011-09
dc.identifier.citation
Crivelli, Tomás; Bouthemy, Patrick; Cernuschi Frias, Bruno; Yao, Jian-Feng; Simultaneous Motion Detection and Background Reconstruction with a Conditional Mixed-State Markov Random Field; Springer; International Journal Of Computer Vision; 94; 3; 9-2011; 295-316
dc.identifier.issn
0920-5691
dc.identifier.uri
http://hdl.handle.net/11336/20226
dc.description.abstract
In this work we present a new way of simultaneously solving the problems of motion detection and background image reconstruction. An accurate estimation of the background is only possible if we locate the moving objects. Meanwhile, a correct motion detection is achieved if we have a good available background model. The key of our joint approach is to define a single random process that can take two types of values, instead of defining two different processes, one symbolic (motion detection) and one numeric (background intensity estimation). It thus allows to exploit the (spatio-temporal) interaction between a decision (motion detection) and an estimation (intensity reconstruction) problem. Consequently, the meaning of solving both tasks jointly, is to obtain a single optimal estimate of such a process. The intrinsic interaction and simultaneity between both problems is shown to be better modeled within the so-called mixed-state statistical framework, which is extended here to account for symbolic states and conditional random fields. Experiments on real sequences and comparisons with existing motion detection methods support our proposal. Further implications for video sequence inpainting will be also discussed.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Springer
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Motion Detection
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Background Reconstruction
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Mixed-State Markov Models
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Conditional Random Fields
dc.subject.classification
Ingeniería de Sistemas y Comunicaciones
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Ingeniería Eléctrica, Ingeniería Electrónica e Ingeniería de la Información
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INGENIERÍAS Y TECNOLOGÍAS
dc.title
Simultaneous Motion Detection and Background Reconstruction with a Conditional Mixed-State Markov Random Field
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2017-07-12T13:19:05Z
dc.journal.volume
94
dc.journal.number
3
dc.journal.pagination
295-316
dc.journal.pais
Países Bajos
dc.description.fil
Fil: Crivelli, Tomás. Institut National de Recherche en Informatique et en Automatique; Francia
dc.description.fil
Fil: Bouthemy, Patrick. Institut National de Recherche en Informatique et en Automatique; Francia
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 Calderon; Argentina
dc.description.fil
Fil: Yao, Jian-Feng. Universite de Rennes I; Francia
dc.journal.title
International Journal Of Computer Vision
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007/s11263-011-0429-z
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1007/s11263-011-0429-z