A new neural network for particle-tracking velocimetry

A new neural network for particle-tracking velocimetry  We describe a new neural network designed to solve the correspondence problem of particle-tracking velocimetry. Given two successive pictures of marker-particles suspended in a fluid, it matches their images by approximately duplicating the fluid motion. We present the results of efficiency tests that reveal the excellence of its performance and its stability with respect to the presence of unmatchable particle images. We compare its success rate in image matching to that of the neural network of Grant and Pan (1995), and observe that it produces better results when the flows have more important changes in direction. It has the important advantages over the latter, of being better adapted to benefit from parallel computing, and of being self-starting, i.e. of not requiring to be taught about the fluid flow in advance. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Experiments in Fluids Springer Journals

A new neural network for particle-tracking velocimetry

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Publisher
Springer-Verlag
Copyright
Copyright © 1999 by Springer-Verlag Berlin Heidelberg
Subject
Engineering; Engineering Fluid Dynamics; Fluid- and Aerodynamics; Engineering Thermodynamics, Heat and Mass Transfer
ISSN
0723-4864
eISSN
1432-1114
D.O.I.
10.1007/s003480050297
Publisher site
See Article on Publisher Site

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