A generalized optical flow constraint and its physical interpretation

Béréziat, Dominique; Herlin, Isabelle; Younes, Laurent

doi:10.1109/cvpr.2000.854890

Cited by 51 publications

(53 citation statements)

References 14 publications

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“…The first assumption is difficult to validate, especially in meteorological images due to the complexity and the heterogeneity of the underlying physical processes. Nevertheless, as shown by several works, the use of the continuity equation in the case of meteorological data is appealing [4,24]. As the brightness consistancy is obviously not verified in that type of images, the equation of continuity provides us with an interesting alternative data-model.…”

Section: Continuity Equationmentioning

confidence: 99%

“…The continuity equation originally introduced in [20] as a data model for motion estimation of intensity time varying images has been since incorporated in several works. It has been considered in the context of fluid imagery either for satellite meteorological images [4,7,24] or for experimental fluid mechanics [23]. It has also been introduced in medical imaging domain to recover 3D deformation fields of the heart [21] or to analyze blood flow [1].…”

Section: Continuity Equationmentioning

confidence: 99%

“…Since several years, the analysis of video sequences showing the evolution of fluid phenomenon gave rise to a great attention from the computer vision community [4,11,15,23,24]. The applications concern domains such as experimental visualization in fluid mechanics, environmental sciences (oceanography, meteorology, ...), or medical imagery.…”

Section: Introductionmentioning

confidence: 99%

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Dense Motion Analysis in Fluid Imagery

Corpetti

Mémin

Pérez

2002

Computer Vision — ECCV 2002

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Abstract. Analyzing fluid motion is essential in number of domains and can rarely be handled using generic computer vision techniques. In this particular application context, we address two distinct problems. First we describe a dedicated dense motion estimator. The approach relies on constraints issuing from fluid motion properties and allows us to recover dense motion fields of good quality. Secondly, we address the problem of analyzing such velocity fields. We present a kind of motionbased segmentation relying on an analytic representation of the motion field that permits to extract important quantities such as singularities, stream-functions or velocity potentials. The proposed method has the advantage to be robust, simple, and fast.

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Section: Continuity Equationmentioning

confidence: 99%

Section: Continuity Equationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dense Motion Analysis in Fluid Imagery

Corpetti

Mémin

Pérez

2002

Computer Vision — ECCV 2002

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“…Neglecting mass exchanges via vertical motion on planar integral boundaries, in [11] and [2], the authors demonstrated that the integration of (3) leads to a mass-conservation model called ICE…”

Section: ) Integrated Continuity Equation (Ice)mentioning

confidence: 99%

Layered Estimation of Atmospheric Mesoscale Dynamics From Satellite Imagery

Héas

Mémin

Papadakis

et al. 2007

IEEE Trans. Geosci. Remote Sensing

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Abstract-In this paper, we address the problem of estimating mesoscale dynamics of atmospheric layers from satellite image sequences. Due to the great deal of spatial and temporal distortions of cloud patterns and because of the sparse 3-D nature of cloud observations, standard dense-motion field-estimation techniques used in computer vision are not well adapted to satellite images. Relying on a physically sound vertical decomposition of the atmosphere into layers, we propose a dense-motion estimator dedicated to the extraction of multilayer horizontal wind fields. This estimator is expressed as the minimization of a global function including data and spatio-temporal smoothness terms. A robust data term relying on the integrated-continuity equation massconservation model is proposed to fit sparse-transmittance observations related to each layer. A novel spatio-temporal smoother derived from large eddy prediction of a shallow-water momentumconservation model is used to build constraints for large-scale temporal coherence. These constraints are combined in a global smoothing framework with a robust second-order smoother, preserving divergent and vorticity structures of the flow. For optimization, a two-stage motion estimation scheme is proposed to overcome multiresolution limitations when capturing the dynamics of mesoscale structures. This alternative approach relies on the combination of correlation and optical-flow observations in a variational context. An exhaustive evaluation of the novel method is first performed on a scalar image sequence generated by direct numerical simulation of a turbulent 2-D flow. By qualitative comparisons, the method is then assessed on a METEOSAT image sequence.

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“…The problem of motion estimation from image sequences has been extensively studied [5,6,7,8,9,10,11,12,13]. The classical computer vision approach relies on a dense estimation of the displacement velocity based on the so-called conservation of gray level-value assumption (also called optical flow constraint): given a pixel (x(t), y(t)) on an image I at time t, we have:…”

Section: Objectivesmentioning

confidence: 99%

Data Assimilation of Satellite Images Within an Oceanographic Circulation Model

Huot

Isambert

Herlin

et al.

2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings

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We tackle the problem of coupling a geophysical simulation model with data coming from image processing. It needs to define the image observation space and to design an operator to transform results from the image space to the model space. In this study, we use a shallow-water oceanographic circulation model developed at MHI. We propose a processing chain first based on an image processing step relying on a dedicated motion estimation operator, and then a data assimilation step of the estimated velocity. We illustrate the method on different results without and with assimilation. OBJECTIVESIn the framework of numerical forecasting for the evolution of geophysical fluid, we are interested in the assimilation of data coming from images. In order to forecast the behavior of geophysical fluids we need: a forecast model to describe the evolution of a state variable (generally it is a non-linear PDE system) and observations spatially and temporally distributed. Data assimilation provides a mathematical solution to combine data and models. During last decades model quality increased significantly. But forecast quality is not directly linked to model quality. To increase forecast quality it is also necessary to increase the amount and the quality of observations. Therefore, images -particularly images coming from spatial remote sensing -provide a huge amount of information. Using images in a data assimilation framework raises several difficulties:1. First it is necessary to define which image space is relevant according to model specialists.2. Then, it is necessary to construct an image operator dedicated to the problematic and coherent to the physical behavior. Moreover it is necessary to define a set of norm in order to quantify the influence of image information along the assimilation process.3. And finally, it is necessary to construct an operator to compare image space and model space.In the study presented in this paper, we are interested in oceanographic circulation forecasting. Oceanographic circulation is ruled by fluid mechanic. Most of oceanographic circulation models are heavy 3D models based on primitive equations [1]. They correspond to an approximation of Navier-Stokes equation associated to a nonlinear state equation coupling salinity, temperature and 3D velocity. Nevertheless, it exists simplified models based on shallow-water approximation [2,3]. They rely on a so called 1.5 layer representation of the ocean: the sea surface is represented by a mixed layer interfaced to the atmosphere and a deeper layer. Equation ruling the circulation are then:

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A generalized optical flow constraint and its physical interpretation

Cited by 51 publications

References 14 publications

Dense Motion Analysis in Fluid Imagery

Dense Motion Analysis in Fluid Imagery

Layered Estimation of Atmospheric Mesoscale Dynamics From Satellite Imagery

Data Assimilation of Satellite Images Within an Oceanographic Circulation Model

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