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In this paper we propose a new motion estimator for image sequences depicting fluid flows. The proposed estimator is based on the Helmholtz decomposition of vector fields. This decomposition consists in representing the velocity field as a sum of a divergence free component and a vorticity free component. The objective is to provide a low-dimensional(More)
In this paper, we present a method for the tracking of fluid flows velocity fields. The technique we propose is formalized within sequential Bayesian filter framework. The filter we propose here combines an Ito diffusion process coming from a stochastic formulation of the vorticity-velocity form of Navier-Stokes equation and discrete measurements extracted(More)
This paper presents a novel non-rigid registration method. The main contribution of the method is the modeling of the vorticity (respectively divergence) of the deformation field using vortex (respectively sink and source) particles. Two parameters are associated with a particle: the vorticity (or divergence) strength and the influence domain. This leads to(More)
This paper proposes an extension of the Weighted Ensemble Kalman filter (WEnKF) proposed by Papadakis et al. (2010) for the assimilation of image observations. The main contribution of this paper consists in a novel formulation of the Weighted filter with the Ensemble Transform Kalman filter (WETKF) incorporating directly as a measurement model a nonlinear(More)
Using motion capture data has nowadays utterly been adopted by video game creators or virtual reality applications. In a context of interactive applications, adapting those data to new situations or producing variants of those motions are known as non trivial tasks. We propose an original method that produces motions that preserve the statistical properties(More)
In this paper, we present a method for the temporal tracking of fluid flow velocity fields. The technique we propose is formalized within a sequential Bayesian filtering framework. The filtering model combines an Ito diffusion process coming from a stochastic formulation of the vorticity-velocity form of the Navier-Stokes equation and discrete measurements(More)
In this paper we present a method for the temporal tracking of fluid flows velocity fields. The technique we propose is formalized within a sequential Bayesian filtering framework. The filtering model combines an Itô diffusion process coming from a stochastic formulation of the vorticity-velocity form of the Navier-Stokes equation and discrete measurements(More)
Nowadays, ocean and atmosphere sciences face a deluge of data from space, in situ monitoring as well as numerical simulations. The availability of these different data sources offer new opportunities, still largely underexploited, to improve the understanding,modeling and reconstruction of geophysical dynamics. The classical way to reconstruct the(More)
We present a novel algorithm for solving the image inpainting problem based on a field of locally interacting particle filters. Image inpainting, also known as image completion, is concerned with the problem of filling image regions with new visually plausible data. In order to avoid the difficulty of solving the problem globally for the region to be(More)