@inproceedings{0f33027d41c8418d82f57dabc3628812,
title = "Parametric dictionary-based velocimetry for echo PIV",
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.",
author = "Ecaterina Bodnariuc and Stefania Petra and Christian Poelma and Christoph Schn{\"o}rr",
year = "2016",
doi = "10.1007/978-3-319-45886-1_27",
language = "English",
isbn = "9783319458854",
volume = "9796 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "332--343",
editor = "B Rosenhahn and B Andres",
booktitle = "Pattern Recognition - Proceedings 38th German Conference 2016",
note = "38th German Conference on Pattern Recognition, GCPR 2016 ; Conference date: 12-09-2016 Through 15-09-2016",
}