Self-similar regularization of optic-flow for turbulent motion estimation

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Based on self-similar models of turbulence, we propose in this paper a multi-scale regularizer in order to provide a closure to the optic-flow estimation problem. Regularization is achieved by constraining motion increments to behave as a self-similar process. The associate constrained minimization problem results in a collection of first-order optic-flow regularizers acting at the different scales. The problem is optimally solved by taking advantage of lagrangian duality. Furthermore, an advantage of using a dual formulation, is that we also infer the regularization parameters. Since, the self-similar model parameters observed in real cases can deviate from theory, we propose to add in the algorithm a bayesian learning stage. The performance of the resulting optic-flow estimator is evaluated on a particle image sequence of a simulated turbulent flow. The self-similar regularizer is also assessed on a meteorological image sequence.

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Available from: Dominique Heitz
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    ABSTRACT: In this paper, Bayesian inference is used to select the most evident Gibbs prior model for motion estimation given some image sequence. The proposed method supplements the maximum a posteriori motion estimation scheme proposed in He¿as et al. (2008). Indeed, in this recent work, the authors have introduced a family of multiscale spatial priors in order to cure the ill-posed inverse motion estimation problem. We propose here a second level of inference where the most likely prior model is optimally chosen given the data by maximization of Bayesian evidence. Model selection and motion estimation are assessed on Meteorological Second Generation (MSG) image sequences. Selecting from images the most evident multiscale model enables the recovery of physical quantities which are of major interest for atmospheric turbulence characterization.
    Full-text · Conference Paper · Aug 2009