Fluid experimental flow estimation based on an optical-flow scheme

Corpetti, Thomas; Heitz, Dominique; Arroyo, G.; Mémin, Étienne; Santa-Cruz, Alina

doi:10.1007/s00348-005-0048-y

Cited by 209 publications

(156 citation statements)

References 37 publications

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“…Figure 1.b represents the exact vorticity map computed from the DSN. We observe that TE-SGS ( Fig.1.e) better retrieves the vorticity map than [14] (Fig. 1.c) or TE.…”

Section: Results Illustrationmentioning

confidence: 76%

“…On the original DNS sequence, [14] and TE give similar results because they use same kind of flow equation. In this sequence, the fluid is incompressible: div w = 0.…”

Section: Results Illustrationmentioning

confidence: 91%

“…5), and with results from the state-of-the-art on fluid motion estimation [14]. In order to evaluate the efficiency of our illumination model, we simulated day light variation effect (see Figure 1) by added a spatially constant intensity offset different at each time t. The offset range goes from −50 to +50 on a 255 gray-level intensity image.…”

Section: Results Illustrationmentioning

confidence: 99%

“…For cloud motion analysis, some authors established empirically the following relation: I ∝ Cdz [14] or I ∝ Cdz −1 [15], where z is the observation depth.…”

Section: Scalar Transport Equation (Te)mentioning

confidence: 99%

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Sub-grid physical optical flow for remote sensing of sandstorm

Cassisa

Simoëns

Prinet

et al. 2010

2010 IEEE International Geoscience and Remote Sensing Symposium

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Satellite observations are currently of major importance in geosciences. Remote sensing is a strong tool to study atmospheric and earth phenomena. In this work, we propose a new motion estimation approach with application to dust storm tracking from remote sensing images. Dynamic natural phenomena in the atmosphere are generally turbulent due to a high Reynolds number. Meteorological images are still poor in time and space resolution compared to the turbulence characteristics of the flow. To tackle this problem, we define a new formulation of the flow equation based on a filtered scalar transport equation. Using Large Eddy Simulation theory, we propose a sub-grid model which incorporates small scale effects as missing (ie non-observed) information of remote sensing images. For day light changes, a uniform brightness variation term is incorporated to the model. We validated our approach on synthetic Direct Numerical Simulation (DNS) of scalar propagation. Promising results are obtained on real MTSAT-1R visible images of a dust storm event over Australia.

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“…Figure 1.b represents the exact vorticity map computed from the DSN. We observe that TE-SGS ( Fig.1.e) better retrieves the vorticity map than [14] (Fig. 1.c) or TE.…”

Section: Results Illustrationmentioning

confidence: 76%

“…On the original DNS sequence, [14] and TE give similar results because they use same kind of flow equation. In this sequence, the fluid is incompressible: div w = 0.…”

Section: Results Illustrationmentioning

confidence: 91%

Section: Results Illustrationmentioning

confidence: 99%

“…For cloud motion analysis, some authors established empirically the following relation: I ∝ Cdz [14] or I ∝ Cdz −1 [15], where z is the observation depth.…”

Section: Scalar Transport Equation (Te)mentioning

confidence: 99%

See 2 more Smart Citations

Sub-grid physical optical flow for remote sensing of sandstorm

Cassisa

Simoëns

Prinet

et al. 2010

2010 IEEE International Geoscience and Remote Sensing Symposium

View full text Add to dashboard Cite

show abstract

“…The optical flow method has been developed for extraction of high-resolution velocity fields from various images of continuous patterns from flow visualization images obtained in laboratories to cloud and ocean images taken by satellites/spacecraft [1][2][3][4][5][6]. The rational foundation for application of the optical flow method to fluid flow measurements is the quantitative connection between the optical flow and the fluid flow velocity for various flow visualizations.…”

Section: Introductionmentioning

confidence: 99%

OpenOpticalFlow: An Open Source Program for Extraction of Velocity Fields from Flow Visualization Images

Liu

2017

JORS

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Multigrid solution of the optical flow system using a combined diffusion‐ and curvature‐based regularizer

Köstler

Ruhnau

Wienands

2008

Numerical Linear Algebra App

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Optical flow techniques are used to compute an approximate motion field in an image sequence. We apply a variational approach for the optical flow using a simple data term but introducing a combined diffusion-and curvature-based regularizer. The same data term arises in image registration problems where a deformation field between two images is computed. For optical flow problems, usually a diffusionbased regularizer should dominate, whereas for image registration a curvature-based regularizer is more appropriate. The combined regularizer enables us to handle optical flow and image registration problems with the same solver and it improves the results of each of the two regularizers used on their own. We develop a geometric multigrid method for the solution of the resulting fourth-order systems of partial differential equations associated with the variational approach for optical flow and image registration problems. The adequacy of using (collective) pointwise smoothers within the multigrid algorithm is demonstrated with the help of local Fourier analysis. Galerkin-based coarse grid operators are applied for an efficient treatment of jumping coefficients. We show some multigrid convergence rates, timings and investigate the visual quality of the approximated motion or deformation field for synthetic and real-world images.Figure 5. First line: two frames of a rotating particle sequence (size 512×512). Second line: the resulting optical flow field for = 500 at the vortex for the diffusion-based regularizer (left) and the curvature-based regularizer (right).

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Fluid experimental flow estimation based on an optical-flow scheme

Cited by 209 publications

References 37 publications

Sub-grid physical optical flow for remote sensing of sandstorm

Sub-grid physical optical flow for remote sensing of sandstorm

OpenOpticalFlow: An Open Source Program for Extraction of Velocity Fields from Flow Visualization Images

Multigrid solution of the optical flow system using a combined diffusion‐ and curvature‐based regularizer

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