International audience; This paper presents a novel, efficient scheme for the analysis of Sea Surface Temperature (SST) ocean images. We consider the estimation of the velocity fields and vorticity values from a sequence of oceanic images. The contribution of this paper... View more
 D. Heitz, E. Me´min, and C. Schno¨rr, “Variational fluid flow measurements from image sequences: synopsis and perspectives,” Experiments in Fluids, vol. 48, pp. 369-393, 2010, 10.1007/s00348-009-0778-3.
 T. Corpetti, P. He´as, E. Me´min, and N. Papadakis, “Pressure image assimilation for atmospheric motion estimation,” Tellus A, vol. 61, no. 1, pp. 160-178, 2009.
 G. Evensen, “The Ensemble Kalman Filter: theoretical formulation and practical implementation,” Ocean Dynamics, vol. 53, pp. 343-367, 2003, 10.1007/s10236-003-0036-9.
 N. Papadakis, E. Me´min, A. Cuzol, and N. Gengembre, “Data assimilation with the weighted ensemble Kalman filter,” Tellus A, vol. 62, no. 5, pp. 673-697, 2010.
 C. H. Bishop, B. J. Etherton, and S. J. Majumdar, “Adaptive Sampling with the Ensemble Transform Kalman Filter. Part I: Theoretical Aspects,” Monthly Weather Review, vol. 129, no. 3, pp. 420-436, 2001.
 M. K. Tippett, J. L. Anderson, C. H. Bishop, T. M. Hamill, and J. S. Whitaker, “Ensemble Square Root Filters,” Monthly Weather Review, vol. 131, no. 7, pp. 1485-1490, 2003.
 P. L. Houtekamer and H. L. Mitchell, “A Sequential Ensemble Kalman Filter for Atmospheric Data Assimilation,” Monthly Weather Review, vol. 129, no. 1, pp. 123-137, 2001.
 T. Corpetti and E. Me´min, “Stochastic models for local optical flow estimation,” Conf. on Scale Space and Variational Methods, SSVM'11, June 2011.