Dense Estimation of Fluid Flows

Abstract : In this paper, we address the problem of estimating and analyzing the motion of fluids in image sequences. Due to the great deal of spatial and temporal distortions that intensity patterns exhibit in images of fluids, the standard techniques from computer vision, originally designed for quasi-rigid motions with stable salient features, are not well adapted in this context. We thus investigate a dedicated minimization-based motion estimator. The cost function to be minimized includes a novel data term relying on an integrated version of the continuity equation of fluid mechanics, which is compatible with large displacements. This term is associated with an original second-order div-curl regularization which prevents the washing out of the salient vorticity and divergence structures. The performance of the resulting fluid flow estimator is demonstrated on meteorological satellite images. In addition, we show how the sequences of dense motion fields we estimate can be reliably used to reconstruct trajectories and to extract the regions of high vorticity and divergence
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Article dans une revue
IEEE Transactions on Pattern Analysis and Machine Intelligence, Institute of Electrical and Electronics Engineers, 2002, 24 (3), pp.365-380. <10.1109/34.990137>
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https://hal.archives-ouvertes.fr/hal-00329724
Contributeur : Thomas Corpetti <>
Soumis le : mardi 13 janvier 2015 - 17:45:52
Dernière modification le : samedi 17 juin 2017 - 01:19:19

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Thomas Corpetti, Mémin Etienne, Patrick Pérez. Dense Estimation of Fluid Flows. IEEE Transactions on Pattern Analysis and Machine Intelligence, Institute of Electrical and Electronics Engineers, 2002, 24 (3), pp.365-380. <10.1109/34.990137>. <hal-00329724v2>

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