Abstract
This paper proposes a novel multi-scale fluid flow data assimilation approach, which integrates and complements the advantages of a Bayesian sequential assimilation technique, the Weighted Ensemble Kalman filter (WEnKF) [12], and an improved multiscale stochastic formulation of the Lucas-Kanade (LK) estimator. The proposed scheme enables to enforce a physically plausible dynamical consistency of the estimated motion fields along the image sequence. © 2012 Springer-Verlag.
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CITATION STYLE
Gorthi, S., Beyou, S., Corpetti, T., & Mémin, E. (2012). Multiscale weighted ensemble Kalman filter for fluid flow estimation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6667 LNCS, pp. 749–760). https://doi.org/10.1007/978-3-642-24785-9_63
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