Assimilation de données images : application au suivi de courbes et de champs de vecteurs

Nicolas Papadakis 1
1 VISTA - Vision spatio-temporelle et active
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : This thesis presents the use of sequential and variational methods for tracking applications in image sequences. These techniques aim at estimating a system state from a dynamical model and a set of noisy and sparse observations. Sequential methods are usually used in computer vision community but limited to small dimensional state spaces. In metorology or oceanography, data assimilation methods are often considered. These approaches enable to deal with high dimensional state spaces. In order to consider the imperfection of complex dynamics visualised in image plane (due to 3D/2D projection), we study the use of imperfect modeling in data assimilation. In our applications, the observations are obtained directly or indirectly from images. We first apply these methods to various tracking problems of computer vision (with an imperfect modelisation of the dynamical model) : curve tracking, fluid motion estimation and joint tracking of curve and motion. We thus show that data assimilation enables to deal with complete data occlusions. Two particular applications where an accurate modelisation of the dynamic can be considered are finally studied : atmospheric layer motion estimation from satellite imagery and control of low order dynamical system from experimental visualisation.
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Nicolas Papadakis. Assimilation de données images : application au suivi de courbes et de champs de vecteurs. Traitement du signal et de l'image. Université Rennes 1, 2007. Français. ⟨tel-00655898⟩

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