Paper Info
Title
Applying Feature Tracking To Particle Image Velocimetry
Abstract
Particle Image Velocimetry (PIV) is a popular approach to flow visualization and measurement 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 displacements of the particles in a digital PIV sequence. These techniques are relatively time-consuming and noise-sensitive. Recently, an optical flow estimation technique developed in machine vision has been successfully used in Particle Image Velocimetry. Feature tracking is an alternative approach to motion estimation, whose application to PIV is proposed and studied in this paper. Two efficient feature tracking algorithms are customized and applied to PIV. The algorithmic solutions of the application are described. In particular, techniques for coherence filtering and interpolation of a velocity field are developed. To assess the proposed and the previous approaches, velocity fields obtained by the different methods are quantitatively compared for numerous synthetic and real PIV sequences. It is concluded that the tracking algorithms offer Particle Image Velocimetry a good alternative to both correlation and optical flow techniques.
Year
DOI
Venue
2003
10.1142/S0218001403002496
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
Keywords
Field
DocType
Particle Image Velocimetry, flow visualization and measurement, feature tracking
Particle tracking velocimetry,Computer vision,Particle image velocimetry,Machine vision,Artificial intelligence,Motion estimation,Optical flow,Velocimetry,Flow visualization,Mathematics,Seeding
Journal
Volume
Issue
ISSN
17
4
0218-0014
Citations 
PageRank 
References 
4
0.59
8
Authors
1
Name
Order
Citations
PageRank
Chetverikov, D.195699.89