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Parametric Dictionary-Based Velocimetry for Echo PIV

  • Ecaterina BodnariucEmail author
  • Stefania Petra
  • Christian Poelma
  • Christoph Schnörr
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9796)

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.

Keywords

Motion Estimation Point Particle Maximal Flow Velocity Sparse Reconstruction Trajectory Matrix 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

EB, SP and CS thank the German Research Foundation (DFG) for its support via grant GRK 1653.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Ecaterina Bodnariuc
    • 1
    Email author
  • Stefania Petra
    • 1
  • Christian Poelma
    • 2
  • Christoph Schnörr
    • 1
  1. 1.Image and Pattern Analysis GroupUniversity of HeidelbergHeidelbergGermany
  2. 2.Laboratory for Aero and HydrodynamicsDelft University of TechnologyDelftThe Netherlands

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