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dc.contributor.authorMoreno-Noguer, Francesc-
dc.contributor.authorSanfeliu, Alberto-
dc.identifier.citation9th Iberoamerican Congress on Pattern Recognition: pp. 37-44 (2004)-
dc.descriptionIberoamerican Congress on Pattern Recognition (CIARP), 2004, Puebla (Mexico)-
dc.description.abstractIn this paper we propose a new technique to perform figure-ground segmentation in image sequences of scenarios with varying illumination conditions. Most of the algorithms in the literature that adapt color, assume smooth color changes over time. On the contrary, our technique formulates multiple hypotheses about the next state of the color distribution (modelled with a Mixture of Gaussians -MoG-), and validates them taking into account shape information of the object. The fusion of shape and color is done in a stage denominated 'sample concentration', that we introduce as a final step to the classical CONDENSATION algorithm. The multiple hypotheses generation, allows for more robust adaptions procedures, and the assumption of gradual change of the lighting conditions over time is no longer necessary.-
dc.description.sponsorshipThis work was supported by projects: 'Navegación autónoma de robots guiados por objetivos visuales' (070-720), 'Supervised learning of industrial scenes by means of an active vision equipped mobile robot.' (J-00063).-
dc.subjectDeformable contours-
dc.subjectColor adaption-
dc.subjectParticle filters-
dc.subjectPattern recognition: Computer vision-
dc.subjectPattern recognition: Object detection-
dc.subjectPattern recognition systems-
dc.subjectComputer vision-
dc.titleAdaptive color model for figure-ground segmentation in dynamic environments-
dc.typecomunicación de congreso-
dc.description.peerreviewedPeer Reviewed-
Appears in Collections:(IRII) Comunicaciones congresos
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