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ÐIn this paper, we address the problem of estimating and analyzing the motion of fluids in image sequences. Due to the great deal of spatial and temporal distortions that intensity patterns exhibit in images of fluids, the standard techniques from Computer Vision, originally designed for quasi-rigid motions with stable salient features, are not well adapted(More)
In this paper we propose a new method to extract the vortices, sources, and sinks from the dense motion field preliminary estimated between two images of a fluid video. This problem is essential in meteorology for instance to identify and track depressions or convective clouds in satellite images. The knowledge of such points allows in addition a compact(More)
The complexity of dynamical laws governing 3D atmospheric flows associated with incomplete and noisy observations makes the recovery of atmospheric dynamics from satellite images sequences very difficult. In this report , we face the challenging problem of estimating physical sound and time-consistent horizontal motion fields at various atmospheric depths(More)
In this paper, we address the problem of fluid motion estimation in image sequences. For such motions, standard dense motion estimation methods, based on intensity conservation and spatial coherence of motion field, are not suitable. This is due to the highly deformable nature of fluid medium. For all applications where fluid motions are to be recovered(More)
In this paper, we present a framework for dynamic consistent estimation of dense motion fields over a sequence of images. The originality of the approach is to exploit recipes related to optimal control theory. This setup allows performing the estimation of an unknown state function according to a given dynamical model and to noisy and incomplete(More)