Parametric dictionary-based velocimetry for echo PIV
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...
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| Main Authors: | , , |
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| Format: | Chapter/Article Conference Paper |
| Language: | English |
| Published: |
27 August 2016
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| In: |
Pattern Recognition
Year: 2016, Pages: 332-343 |
| DOI: | 10.1007/978-3-319-45886-1_27 |
| Subjects: | |
| Online Access: | Verlag, Volltext: http://dx.doi.org/10.1007/978-3-319-45886-1_27 Verlag, Volltext: https://link.springer.com/chapter/10.1007/978-3-319-45886-1_27 
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| Author Notes: | Ecaterina Bodnariuc, Stefania Petra, Christian Poelma, Christoph Schnörr |
MARC
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| 520 | |a 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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