CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2013 vol.35 Issue No.06 - June

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Issue No.06 - June (2013 vol.35)

pp: 1343-1356

Patrick Héas , INRIA, Rennes

Cédric Herzet , INRIA, Rennes

Etienne Mémin , INRIA, IRSTEA, Rennes

Dominique Heitz , IRSTEA, Rennes

Pablo D. Mininni , University of Buenos-Aires, Buenos Aires and National Center for Atmospheric Research, Buenos Aires

ABSTRACT

Based on physical laws describing the multiscale structure of turbulent flows, this paper proposes a regularizer for fluid motion estimation from an image sequence. Regularization is achieved by imposing some scale invariance property between histograms of motion increments computed at different scales. By reformulating this problem from a Bayesian perspective, an algorithm is proposed to jointly estimate motion, regularization hyperparameters, and to select the most likely physical prior among a set of models. Hyperparameter and model inference are conducted by posterior maximization, obtained by marginalizing out non--Gaussian motion variables. The Bayesian estimator is assessed on several image sequences depicting synthetic and real turbulent fluid flows. Results obtained with the proposed approach exceed the state-of-the-art results in fluid flow estimation.

INDEX TERMS

Bayesian methods, Optimization, Vectors, Estimation, Motion estimation, Computational modeling, Optical imaging, Bayesian model selection, Optic flow, turbulence, robust estimation, constrained optimization

CITATION

Patrick Héas, Cédric Herzet, Etienne Mémin, Dominique Heitz, Pablo D. Mininni, "Bayesian Estimation of Turbulent Motion",

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