Bayesian Estimation of Turbulent Motion

• Autores: Héas, Patrick; Herzet, Cédric; Mémin, Etienne; Heitz, Dominique; Mininni, Pablo Daniel
• Año de publicación: 2013
• Idioma: inglés
• Tipo de recurso: artículo
• Materia: BAYESIAN MODEL SELECTION; CONSTRAINED OPTIMIZATION; OPTIC FLOW; ROBUST ESTIMATION; TURBULENCE
• Institución: Consejo Nacional de Investigaciones Científicas y Técnicas

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.
Fil: Héas, Patrick. Institut National de Recherche en Informatique et en Automatique; Francia
Fil: Herzet, Cédric. Institut National de Recherche en Informatique et en Automatique; Francia
Fil: Mémin, Etienne. Institut National de Recherche en Informatique et en Automatique; Francia
Fil: Heitz, Dominique. Institut National de Recherche en Informatique et en Automatique; Francia
Fil: Mininni, Pablo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. National Center for Atmospheric Research; Estados Unidos de América;