Optical flow for incompressible turbulence motion estimation

Chen, Xu; Zillé, Pascal; Shao, Lei; Corpetti, Thomas

doi:10.1007/s00348-014-1874-6

Cited by 49 publications

(29 citation statements)

References 42 publications

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“…Although the first-order Tikhonov constraint functional is physically plausible, it is not derived from the first principles. A recent effort has been made by incorporating certain physical mechanisms in turbulent flows into the constraints in optical flow computations (Cassisa et al 2011;Zille et al 2014;Chen et al 2015).…”

Section: Variational Solutionmentioning

confidence: 99%

Comparison between optical flow and cross-correlation methods for extraction of velocity fields from particle images

et al. 2015

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Section: Variational Solutionmentioning

confidence: 99%

Comparison between optical flow and cross-correlation methods for extraction of velocity fields from particle images

et al. 2015

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“…This gave the physical foundation needed since the brightness constancy assumption is not based on any physical principle. Following Cassisa et al [7] and Chen et al [8], we propose a data term based on Large Eddy Simulation (LES) decomposition [9] of the transport equation. We assume that the image intensity function is related to some passive scalar field concentration .…”

Section: Sgsd Modelmentioning

confidence: 99%

“…Authors in [7] considered / to be related to turbulent viscosity / = −0 ∇ ̅ where 0 is a turbulent diffusion coefficient and suggested a model to estimate it, while in [8] authors took a different route and proposed to model / directly. Both models considerably improved the results.…”

Section: Sgsd Modelmentioning

confidence: 99%

Application of optical flow for river velocimetry

Khalid

Pénard

Mémin³

2017

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

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We present a new method for the application of imagebased river velocimetry. Our method is dedicated to high velocity river flows and it is based on an optical flow scheme. Unlike traditional Particle Image Velocimetry (PIV) methods, optical flow provides dense estimations and it is highly adaptable to different applications. We therefore propose a new physics-based optical flow derived from scalar transport equation and augmented with a weighted diffusion term. The results are evaluated on a Lagrangian basis by means of trajectories reconstruction. The trajectories of few passive particles of interest are reconstructed, based on the successive estimated vector fields. Our method outperformed traditional PIV-based and original optical flow methods.

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“…Fluid-dedicated estimators based on optical flow have been elaborated in several studies [5], [6], [7], [8], [9]. For instance, a data term based on the integrated continuity equation and a second-order div-curl regularizer has been proposed in [5], for preserving the divergence and the vorticity of the flow.…”

Section: Introductionmentioning

confidence: 99%

“…A generalized transport equation is applied in [10], which also combines cross-correlation with optical flow approach to make the estimation more robust. Recently, the authors of [9] suggest to replace the optical flow constraint with a structural sub-grid transport equation, which takes into account the small-scale velocity component of the turbulent flow. These estimators provide decent results on fluid motion analysis.…”

Section: Introductionmentioning

confidence: 99%

Sea Surface Flow Estimation via Ensemble-based Variational Data Assimilation

Cai

Mémin

Yin

et al. 2018

2018 Annual American Control Conference (ACC)

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Abstract-In this paper, we propose a data assimilation method for consistently estimating the velocity fields from a whole image sequence depicting the evolution of sea surface temperature transported by oceanic surface flow. The estimator is conducted through an ensemble-based variational data assimilation, which is designed by combining the advantages of two approaches: the ensemble Kalman filter and the variational data assimilation. This idea allows us to obtain the optimal initial condition as well as the full system trajectory. In order to extract the velocity fields from fluid images, a surface quasi-geostrophic model representing the generic evolution of the temperature field of the flow, and the optical flow constraint equation derived from the image intensity constancy assumption, are involved in the assimilation context. Numerical experimental evaluation is presented on a synthetic fluid image sequence. The results indicate good performance and efficiency of the proposed estimator.

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Optical flow for incompressible turbulence motion estimation

Cited by 49 publications

References 42 publications

Comparison between optical flow and cross-correlation methods for extraction of velocity fields from particle images

Comparison between optical flow and cross-correlation methods for extraction of velocity fields from particle images

Application of optical flow for river velocimetry

Sea Surface Flow Estimation via Ensemble-based Variational Data Assimilation

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