Fast and accurate PIV computation using highly parallel iterative correlation maximization

Champagnat, Frédéric; Plyer, Aurélien; Besnerais, Guy Le; Leclaire, Benjamin; Davoust, Samuel; Sant, Yves Le

doi:10.1007/s00348-011-1054-x

Cited by 160 publications

(117 citation statements)

References 20 publications

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“…Using an NVIDIA GTX480 board, this method typically computes a stereo vector field from 1200 × 800 pixels images in less than 0.25 s (see, respectively Leclaire et al 2009;Champagnat et al 2011, for the new stereo paradigm and for the principle of the GPU implementation in the two-component PIV case). The interrogation window (IW) size is set to 31 pixels and, accordingly to the spatial resolution yielded by the algorithm (see Champagnat et al 2011), the vector fields are sampled every 30 pixels, i.e. every 5.4 mm in the object plane.…”

Section: High-speed Stereo Piv Setupmentioning

confidence: 99%

Dynamics ofm= 0 andm= 1 modes and of streamwise vortices in a turbulent axisymmetric mixing layer

2012

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The near field of a Reynolds number Re 0 = 2.14 × 10 5 and low-Mach-number cylindrical jet has been investigated by means of a high-speed stereo PIV setup that provides the spatio-temporal velocity field in a transverse plane, two diameters downstream of the jet exit. Proper orthogonal decomposition (POD) and spatiotemporal correlations are used to identify some of the main dynamical features of this flow. We show that the flow is dominated by streamwise vortices whose production and spatial organization can be related to m = 0 and m = 1 perturbations, and to the mean shear of the mixing layer. A dynamical scenario is proposed which describes this interaction, in accordance with our observations.

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Section: High-speed Stereo Piv Setupmentioning

confidence: 99%

Dynamics ofm= 0 andm= 1 modes and of streamwise vortices in a turbulent axisymmetric mixing layer

2012

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“…All velocity fields were calculated using an iterative Lucas-Kanade optical flow algorithm developed by Champagnat et al (2011), referred as FOLKI-SPIV. This algorithm performs precise and fast computation of dense PIV vector fields through GPU implementation.…”

Section: Instrumentationmentioning

confidence: 99%

Experimental and numerical response of a high-Reynolds-number M=0.6 jet to a Plasma Synthetic Jet actuator

Chedevergne

Léon

Bodoc

et al. 2015

International Journal of Heat and Fluid Flow

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“…In recent years, there have also been many implementations based on FPGA [5,15,28,[45][46][47][48] and graphic processor units (GPU) [6,8,[49][50][51]. The results of a comparative study of both technologies for real-time optical flow computation are presented in [52].…”

Section: Parallelization Of the Optical Flowmentioning

confidence: 99%

“…Determining optical flow is a subject that has been studied by means of computers for several decades, and it has been employed in applications, such as (a) three-http://jivp.eurasipjournals.com/content/2014/1/18 dimensional image segmentation [2], (b) support for navigation of autonomous robots or, in general, the detection of obstacles to avoid collisions [3][4][5][6], (c) synchronization and/or 'matching' of video scenes [7], and (d) fluid dynamics analysis [8]. In any case, the problem is computationally very complex, so most of the proposed solutions are based on strong simplifications adapted to the technology available at the time or to the specific applications they intend to solve.…”

Section: Introductionmentioning

confidence: 99%

Parallelization of the optical flow computation in sequences from moving cameras

et al. 2014

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This paper presents a flexible and scalable approach to the parallelization of the computation of optical flow. This approach is based on data parallel distribution. Images are divided into several subimages processed by a software pipeline while respecting dependencies between computation stages. The parallelization has been implemented in three different infrastructures: shared, distributed memory, and hybrid to show its conceptual flexibility and scalability. A significant improvement in performance was obtained in all three cases. These versions have been used to compute the optical flow of video sequences taken in adverse conditions, with a moving camera and natural-light conditions, on board a conventional vehicle traveling on public roads. The parallelization adopted has been developed from the analysis of dependencies presented by the well-known Lucas-Kanade algorithm, using a sequential version developed at the University of Porto as the starting point.

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Fast and accurate PIV computation using highly parallel iterative correlation maximization

Cited by 160 publications

References 20 publications

Dynamics ofm= 0 andm= 1 modes and of streamwise vortices in a turbulent axisymmetric mixing layer

Dynamics ofm= 0 andm= 1 modes and of streamwise vortices in a turbulent axisymmetric mixing layer

Experimental and numerical response of a high-Reynolds-number M=0.6 jet to a Plasma Synthetic Jet actuator

Parallelization of the optical flow computation in sequences from moving cameras

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