Particle image velocimetry by feature tracking

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

Particle Image Velocimetry (PIV) is a popular approach to flow visualisation in hydro-and aerodynamic studies and applications. The fluid is seeded with particles that follow the flow and make it visible. Traditionally, correlation techniques have been used to estimate the dis- placements of the particles in a digital PIV sequence. In this paper, two efficient feature tracking algorithms are customised and applied to PIV. The algorithmic solutions of the application are described. Techniques for coherence filtering and interpolation of a velocity field are developed. Ex- perimental results are given, demonstrating that the tracking algorithms offer Particle Image Velocimetry a good alternative to the existing tech- niques. 1

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages325-332
Number of pages8
Volume2124
ISBN (Print)9783540425137
Publication statusPublished - 2001
Event9th International Conference on Computer Analysis of Images and Patterns, CAIP 2001 - Warsaw, Poland
Duration: Sep 5 2001Sep 7 2001

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2124
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other9th International Conference on Computer Analysis of Images and Patterns, CAIP 2001
CountryPoland
CityWarsaw
Period9/5/019/7/01

Fingerprint

Feature Tracking
Velocity measurement
Flow visualization
Flow Visualization
Interpolation
Aerodynamics
Image Sequence
Velocity Field
Placement
Fluids
Filtering
Interpolate
Fluid
Alternatives
Experimental Results
Estimate

Keywords

  • Feature tracking
  • Motion analysis
  • Particle image velocimetry

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Chetverikov, D. (2001). Particle image velocimetry by feature tracking. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2124, pp. 325-332). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2124). Springer Verlag.

Particle image velocimetry by feature tracking. / Chetverikov, D.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 2124 Springer Verlag, 2001. p. 325-332 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2124).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Chetverikov, D 2001, Particle image velocimetry by feature tracking. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 2124, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2124, Springer Verlag, pp. 325-332, 9th International Conference on Computer Analysis of Images and Patterns, CAIP 2001, Warsaw, Poland, 9/5/01.
Chetverikov D. Particle image velocimetry by feature tracking. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 2124. Springer Verlag. 2001. p. 325-332. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Chetverikov, D. / Particle image velocimetry by feature tracking. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 2124 Springer Verlag, 2001. pp. 325-332 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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