Identification of velocity fields for geophysical fluids from a sequence of images

Auroux, Didier; Fehrenbach, Jérôme

doi:10.1007/s00348-010-0926-9

Cited by 14 publications

(13 citation statements)

References 28 publications

(21 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Past work for computing fiuid optical flows can be found, for example in [2,3,21,25], and in each case, the ability to capture the structures of vortices rests on extracting discontinuity information from the flow field. It is well-known that the regularizing of an energy functional with the total variation of the input function results in solutions that are approximately piecewise constant and that the jump discontinuities in the signal are enhanced (see [26,27] for the original formulation and e.g.…”

Section: ) Jnmentioning

confidence: 99%

An Optical Flow Approach to Analyzing Species Density Dynamics and Transport

Holloway¹

2012

JCM

View full text Add to dashboard Cite

Classical optical fiow techniques were developed for computing virtual motion fields between two images of the same scene, assuming conservation of intensity and a smoothness of the flow field. If these assumptions are dropped, such techniques can be adapted to compute apparent flows between time snapshots of data that do not come from images, even if these fiows are turbulent and divergent, as in the case of flows representing complex spatiotemporal dynamics. While imaging methods have been used to analyze dynamics in experimental applications, they are only beginning to be applied to dynamics computations in settings outside the laboratory, for example in the analysis of species population dynamics from satellite data. In this work we present a variational optical flow approach based on the continuity equation and total variation regularization for computing the flow fields between population densities generated from a two-species predator-prey model for phyto-and Zooplankton interactions. Given the time-varying vector fields produced from the optical flow, computational methods from dynamical systems can be employed to study pseudo-barriers present in the species interaction. This method allows to measure the mixing of the species, as well as the transport of the populations throughout the domain.Mathematics subject classification: 49N45, 49M99, 37M25, 65P99, 68T45.

show abstract

Section: ) Jnmentioning

confidence: 99%

An Optical Flow Approach to Analyzing Species Density Dynamics and Transport

Holloway¹

2012

JCM

View full text Add to dashboard Cite

show abstract

“…In recent years, fluid motion estimation methods have been proposed that combine appearance based model with optical flow framework. These methods [18,19,20,21] add constraints to prefer the fluid like motion in the energy minimization process. Corpetti et al [19], for example, apply a divergence-curltype smoothness to replace the original smoothness term in optical flow framework.…”

Section: Related Workmentioning

confidence: 99%

Dense Motion Estimation for Smoke

Chen

Hall

2017

Computer Vision – ACCV 2016

View full text Add to dashboard Cite

Abstract. Motion estimation for highly dynamic phenomena such as smoke is an open challenge for Computer Vision. Traditional dense motion estimation algorithms have difficulties with non-rigid and large motions, both of which are frequently observed in smoke motion. We propose an algorithm for dense motion estimation of smoke. Our algorithm is robust, fast, and has better performance over different types of smoke compared to other dense motion estimation algorithms, including state of the art and neural network approaches. The key to our contribution is to use skeletal flow, without explicit point matching, to provide a sparse flow. This sparse flow is upgraded to a dense flow. In this paper we describe our algorithm in greater detail, and provide experimental evidence to support our claims.

show abstract

“…The choice of the regularization term depends on the application field [Vigan et al (2000) for oceanography, Amodei and Benbourhim (1991) for wind field]. This problem can be summarized by finding that minimizes F defined by where is a spatial regularization function (see Auroux and Fehrenbach, 2008) and is the regularization factor. The minimization problem is treated either by optimal control or by vector spline (Amodei, 1993; Suter, 1994; Isambert et al, 2007).…”

Section: Images As Source Of Pseudo‐observation For Data Assimilationmentioning

confidence: 99%

“…where S : R 2 → R 2 is a spatial regularization function (see Auroux and Fehrenbach, 2008) and λ ∈ R is the regularization factor. The minimization problem (12) is treated either by optimal control or by vector spline (Amodei, 1993;Suter, 1994;Isambert et al, 2007).…”

Section: Frame-to-frame Motion Estimatormentioning

confidence: 99%

Assimilation of image sequences in numerical models

Titaud¹,

Vidard²,

Souopgui³

et al. 2010

Tellus A: Dynamic Meteorology and Oceanography

View full text Add to dashboard Cite

Understanding and forecasting the evolution of geophysical fluids is a major scientific and societal challenge. Forecasting algorithms should take into account all the available information on the considered dynamic system. The variational data assimilation (VDA) technique combines all these informations in an optimality system (O.S.) in a consistent way to reconstruct the model inputs. VDA is currently used by the major meteorological centres. During the last two decades about 30 satellites were launched to improve the knowledge of the atmosphere and of the oceans. They continuously provide a huge amount of data that are still underused by numerical forecast systems. In particular, the dynamic evolution of certain meteorological or oceanic features (such as eddies, fronts, etc.) that the human vision may easily detect is not optimally taken into account in realistic applications of VDA. Image Assimilation in VDA framework can be performed using ‘pseudo‐observation’ techniques: they provide apparent velocity fields, which are assimilated as classical observations. These measurements are obtained by certain external procedures, which are decoupled with the considered dynamic system. In this paper, we suggest a more consistent approach, which directly incorporates image sequences into the O.S.

show abstract

Identification of velocity fields for geophysical fluids from a sequence of images

Cited by 14 publications

References 28 publications

An Optical Flow Approach to Analyzing Species Density Dynamics and Transport

An Optical Flow Approach to Analyzing Species Density Dynamics and Transport

Dense Motion Estimation for Smoke

Assimilation of image sequences in numerical models

Contact Info

Product

Resources

About