Motion Estimation Under Location Uncertainty, Application To Large-Scale Characterization Of A Mixing Layer
Résumé
We propose a novel motion estimation from image sequences method to study turbulent flows. This method consists in modeling the luminance motions between successive frames as a new stochastic scalar transport equation. Thanks to stochastic formalism the motion field is separated into a large-scale smooth component and a random small-scale component. This decomposition gives rise to the mentioned new stochastic transport equation that takes into account the interactions between observed large scales and unresolved ones. This equation provides a new data term for optical flow algorithm. We then resolved the aperture problem with local Lucas-Kanade approach. We show that the method improves the motion estimation on a synthetic case. Then the method is tested on real large-scale observations of a mixing layer. Two kinds of tracers are compared and a new lighting system based on LED technology is employed to perform this large-scale experiment.
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