Abstract
We introduce a novel motion estimation approach for Echo PIV for the laminar and steady flow model. We mathematically formalize the motion estimation problem as a parametrization of a dictionary of particle trajectories by the physical flow parameter. We iteratively refine this unknown parameter by subsequent sparse approximations. We show smoothness of the adaptive flow dictionary that is a key for a provably convergent numerical scheme. We validate our approach on real data and show accurate velocity estimation when compared to the state-of-the-art cross-correlation method.
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Acknowledgements
EB, SP and CS thank the German Research Foundation (DFG) for its support via grant GRK 1653.
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Bodnariuc, E., Petra, S., Poelma, C., Schnörr, C. (2016). Parametric Dictionary-Based Velocimetry for Echo PIV. In: Rosenhahn, B., Andres, B. (eds) Pattern Recognition. GCPR 2016. Lecture Notes in Computer Science(), vol 9796. Springer, Cham. https://doi.org/10.1007/978-3-319-45886-1_27
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DOI: https://doi.org/10.1007/978-3-319-45886-1_27
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