A Multi-scale Vector Spline Method for Estimating the Fluids Motion on Satellite Images

Isambert, Till; Berroir, Jean-Paul; Herlin, Isabelle

doi:10.1007/978-3-540-88693-8_49

Cited by 6 publications

(3 citation statements)

References 16 publications

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“…For fluid flows analysis and fluid motion estimation from image sequences spline basis functions minimizing a second order div-curl constraint (see equation 25 section 4.1) have been proposed (Amodei & Benbourhim, 1991;Suter, 1994;Isambert et al, 2008). Compared to vortex particles these basis functions have the drawback to impose strictly an empirical kinematics constraint that is not built from physical considerations.…”

Section: Globalized Local Smoothing: Ritz Methodsmentioning

confidence: 99%

Variational fluid flow measurements from image sequences: synopsis and perspectives

2009

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Variational approaches to image motion segmentation has been an active field of study in image processing and computer vision for two decades. We present a short overview over basic estimation schemes and report in more detail recent modifications and applications to fluid flow estimation. Key properties of these approaches are illustrated by numerical examples. We outline promising research directions and point out the potential of variational techniques in combination with correlation-based PIV methods, for improving the consistency of fluid flow estimation and simulation.

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Section: Globalized Local Smoothing: Ritz Methodsmentioning

confidence: 99%

Variational fluid flow measurements from image sequences: synopsis and perspectives

2009

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“…In the Image Processing literature, motion fields are most often inferred from an equation describing the transport of pixel brightness by velocity (for instance the optical flow constraint equation used in (Horn and Schunk, 1981) and (Isambert et al, 2008), or the mass conservation equation of (Béréziat et al, 2000). By nature, this transport equation is under-constrained to retrieve the two components of motion on 2D image data and spatial regularization techniques are used to define a well-posed problem with a unique solution.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Motion Estimation by Assessing the Errors on the Evolution Equation

Herlin

Béréziat²,

Mercier

2012

Proceedings of the International Conference on Computer Vision Theory and Applications

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Abstract:Image assimilation methods are nowadays widely used to retrieve motion from image sequences with heuristics on the underlying dynamics. A mathematical model on the temporal evolution of the motion field has to be chosen, according to these heuristics, that approximately describes the evolution of the velocity at a pixel over the sequence. In order to quantify this approximation, we add an error term in the evolution equation of the motion field and design a weak formulation of 4D-Var image assimilation. The designed cost function simultaneously depends on the initial motion field and on the error value at each time step. The BFGS solver performs minimization to retrieve both motion field and errors. The method is evaluated and quantified on twin experiments, as no ground truth would be available for real data. The results demonstrate that the motion field is better estimated thanks to the error control.

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“…A Tikhonov regularization is then applied, relying on regularity heuristics. In [4,6], authors use a regularity constraint based on the irrotational and solenoidal description of the motion field. To apply the approach on fluid motion, authors [2,4] also propose to replace the usual luminance conservation hypothesis by a mass conservation equation.…”

Section: Introduction and State Of The Artmentioning

confidence: 99%

Estimating Apparent Motion on Satellite Acquisitions with a Physical Dynamic Model

Huot

Herlin

Mercier

et al. 2010

2010 20th International Conference on Pattern Recognition

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The paper presents a motion estimation method based on data assimilation in a dynamic model, named Image Model, expressing the physical evolution of a quantity observed on the images. The application concerns the retrieval of apparent surface velocity from a sequence of satellite data, acquired over the ocean. The Image Model includes a shallow-water approximation for the dynamics of the velocity field (the evolution of the two components of motion are linked by the water layer thickness) and a transport equation for the image field. For retrieving the surface velocity, a sequence of Sea Surface Temperature (SST) acquisitions is assimilated in the Image Model with a 4D-Var method. This is based on the minimization of a cost function including the discrepancy between model outputs and SST data and a regularization term. Several types of regularization norms have been studied. Results are discussed to analyze the impact of the different components of the assimilation system.

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A Multi-scale Vector Spline Method for Estimating the Fluids Motion on Satellite Images

Cited by 6 publications

References 16 publications

Variational fluid flow measurements from image sequences: synopsis and perspectives

Variational fluid flow measurements from image sequences: synopsis and perspectives

Improvement of Motion Estimation by Assessing the Errors on the Evolution Equation

Estimating Apparent Motion on Satellite Acquisitions with a Physical Dynamic Model

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