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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)
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)
This paper proposes a novel multi-scale fluid flow data assimilation approach, which integrates and complements the advantages of a Bayesian sequential assimilation technique, the Weighted Ensemble Kalman filter (WEnKF) [12], and an improved multiscale stochastic formulation of the Lucas-Kanade (LK) estimator. The proposed scheme enables to enforce a(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)
In this paper a new and original technique to animate a crowd of human beings is presented. Following the success of data-driven animation models (such as motion capture) in the context of articulated figures control, we propose to derivate a similar type of approach for crowd motions. In our framework, the motion of the crowds are represented as a time(More)