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Journal Articles IEEE Transactions on Pattern Analysis and Machine Intelligence Year : 2013

Bayesian estimation of turbulent motion

Abstract

Based on physical laws describing the multi-scale structure of turbulent flows, this article 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 hyper-parameters, and to select the most likely physical prior among a set of models. Hyper-parameter and model inference is 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
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Dates and versions

hal-00745814 , version 1 (26-10-2012)

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Patrick Héas, Cédric Herzet, Etienne Mémin, Dominique Heitz, Pablo D. Mininni. Bayesian estimation of turbulent motion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35 (6), pp.1343-1356. ⟨10.1109/TPAMI.2012.232⟩. ⟨hal-00745814⟩
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