A Probabilistic Contour Observer for Online Visual Tracking
SIAM Journal on Imaging Sciences
Multiscale weighted ensemble kalman filter for fluid flow estimation
SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
On performance analysis of optical flow algorithms
Proceedings of the 15th international conference on Theoretical Foundations of Computer Vision: outdoor and large-scale real-world scene analysis
Divergence-free motion estimation
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Inflow and initial conditions for direct numerical simulation based on adjoint data assimilation
Journal of Computational Physics
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In this paper, a variational technique derived from optimal control theory is used in order to realize a dynamically consistent motion estimation of a whole fluid image sequence. The estimation is conducted through an iterative process involving a forward integration of a given dynamical model followed by a backward integration of an adjoint evolution law. By combining physical conservation laws and image observations, a physically grounded temporal consistency is imposed, and the quality of the motion estimation is significantly improved. The method is validated on two synthetic image sequences provided by numerical simulation of fluid flows and on real world meteorological examples.