NDLI: Fast and accurate PIV computation using highly parallel iterative correlation maximization

Content Provider	Springer Nature Link
Author	Champagnat, F. Plyer, A. Le Besnerais, G. Leclaire, B. Davoust, S. Le Sant, Y.
Copyright Year	2011
Abstract	Our contribution deals with fast computation of dense two-component (2C) PIV vector fields using Graphics Processing Units (GPUs). We show that iterative gradient-based cross-correlation optimization is an accurate and efficient alternative to multi-pass processing with FFT-based cross-correlation. Density is meant here from the sampling point of view (we obtain one vector per pixel), since the presented algorithm, folki, naturally performs fast correlation optimization over interrogation windows with maximal overlap. The processing of 5 image pairs (1,376 × 1,040 each) is achieved in less than a second on a NVIDIA Tesla C1060 GPU. Various tests on synthetic and experimental images, including a dataset of the 2nd PIV challenge, show that the accuracy of folki is found comparable to that of state-of-the-art FFT-based commercial softwares, while being 50 times faster.
Starting Page	1169
Ending Page	1182
Page Count	14
File Format	PDF
ISSN	07234864
Journal	Experiments in Fluids
Volume Number	50
Issue Number	4
e-ISSN	14321114
Language	English
Publisher	Springer-Verlag
Publisher Date	2011-03-17
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Engineering Thermodynamics, Heat and Mass Transfer Engineering Fluid Dynamics Fluid- and Aerodynamics
Content Type	Text
Resource Type	Article
Subject	Fluid Flow and Transfer Processes Physics and Astronomy Mechanics of Materials Computational Mechanics

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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