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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)
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)
The complexity of dynamical laws governing 3D atmospheric flows associated with incomplete and noisy observations make the recovery of atmospheric dynamics from satellite images sequences very difficult. In this paper, we face the challenging problem of estimating physical sound and time-consistent horizontal motion fields at various atmospheric depths for(More)
—This paper focuses on the tracking and analysis of convective cloud systems from Meteosat Second Generation images. The highly deformable nature of convective clouds, the complexity of the physical processes involved, and also the partially hidden measurements available from image data make difficult the direct use of conventional image-analysis techniques(More)