Parametric dictionary-based velocimetry for echo PIV

Ecaterina Bodnariuc*, Stefania Petra, Christian Poelma, Christoph Schnörr

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Title of host publicationPattern Recognition - Proceedings 38th German Conference 2016
EditorsB Rosenhahn, B Andres
Place of PublicationCham, Switzerland
Volume9796 LNCS
ISBN (Print)9783319458854
Publication statusPublished - 2016
Event38th German Conference on Pattern Recognition - Hannover, Germany
Duration: 12 Sep 201615 Sep 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9796 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349


Conference38th German Conference on Pattern Recognition
Abbreviated titleGCPR 2016


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