IFIBA   22255
INSTITUTO DE FISICA DE BUENOS AIRES
Unidad Ejecutora - UE
artículos
Título:
Bayesian estimation of turbulent motion
Autor/es:
P. HÉAS; C. HERZET; E. MÉMIN; D. HEITZ; P. D. MININNI
Revista:
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Editorial:
IEEE COMPUTER SOC
Referencias:
Lugar: Los Alamitos, CA, USA; Año: 2013 vol. 35 p. 1343 - 1356
ISSN:
0162-8828
Resumen:
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.