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| Campo DC | Valor | Lengua/Idioma |
|---|---|---|
| dc.creator | Crivelli, Tomás | - |
| dc.creator | Cernuschi Frias, Bruno | - |
| dc.creator | Bouthemy, Patrick | - |
| dc.creator | Yao, Jian-Feng | - |
| dc.date | 2017-07-03T21:19:09Z | - |
| dc.date | 2017-07-03T21:19:09Z | - |
| dc.date | 2010-11 | - |
| dc.date | 2017-07-03T16:49:43Z | - |
| dc.date.accessioned | 2019-04-29T15:31:58Z | - |
| dc.date.available | 2019-04-29T15:31:58Z | - |
| dc.date.issued | 2010-11 | - |
| dc.identifier | Crivelli, Tomás; Cernuschi Frias, Bruno; Bouthemy, Patrick; Yao, Jian-Feng; Mixed-state causal modeling for statistical KL-based motion texture tracking; Elsevier Science; Pattern Recognition Letters; 31; 14; 11-2010; 2286-2294 | - |
| dc.identifier | 0167-8655 | - |
| dc.identifier | http://hdl.handle.net/11336/19432 | - |
| dc.identifier | CONICET Digital | - |
| dc.identifier | CONICET | - |
| dc.identifier.uri | http://rodna.bn.gov.ar:8080/jspui/handle/bnmm/295894 | - |
| dc.description | We 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. | - |
| dc.description | Fil: Crivelli, Tomás. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Electronica; Argentina | - |
| dc.description | Fil: Cernuschi Frias, Bruno. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Argentino de Matemática Alberto Calderon; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Electronica; Argentina | - |
| dc.description | Fil: Bouthemy, Patrick. Irisa, Inria, Rennes, Francia; | - |
| dc.description | Fil: Yao, Jian-Feng. Institut National de Recherche en Informatique et en Automatique; Francia | - |
| dc.format | application/pdf | - |
| dc.format | application/pdf | - |
| dc.language | eng | - |
| dc.publisher | Elsevier Science | - |
| dc.relation | info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0167865510002035 | - |
| dc.relation | info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.patrec.2010.06.016 | - |
| dc.rights | info:eu-repo/semantics/restrictedAccess | - |
| dc.rights | https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ | - |
| dc.source | reponame:CONICET Digital (CONICET) | - |
| dc.source | instname:Consejo Nacional de Investigaciones Científicas y Técnicas | - |
| dc.source | instacron:CONICET | - |
| dc.subject | Mixed-state Markov models | - |
| dc.subject | Motion textures | - |
| dc.subject | Visual tracking | - |
| dc.subject | Kullback-Leibler divergence | - |
| dc.subject | Ingeniería de Sistemas y Comunicaciones | - |
| dc.subject | Ingeniería Eléctrica, Ingeniería Electrónica e Ingeniería de la Información | - |
| dc.subject | INGENIERÍAS Y TECNOLOGÍAS | - |
| dc.title | Mixed-state causal modeling for statistical KL-based motion texture tracking | - |
| dc.type | info:eu-repo/semantics/article | - |
| dc.type | info:eu-repo/semantics/publishedVersion | - |
| dc.type | info:ar-repo/semantics/articulo | - |
| Aparece en las colecciones: | CONICET | |
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