Variational Approaches to Image Fluid Flow Estimation with Physical Priors

Vlasenko, Andrey; Schnörr, Christoph

doi:10.1007/978-3-642-01106-1_25

Cited by 6 publications

(9 citation statements)

References 18 publications

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“…Apparently, other regularizers, such as physical priors, are needed when you estimate the fluid velocities through optical flow techniques. In and , divergence‐free constraints were chosen. There exists a defect in these methods that both of them require complex discretization steps because of the high‐order regularizers.…”

Section: Related Workmentioning

confidence: 99%

Physical‐based spatio‐temporal resolution enhancement of scalar data for fluid visualization

Liqun

Chen

2016

Computer Animation & Virtual

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We present a method that improves the spatio‐temporal resolution of original data with fluid scalar data. The basis of the method, velocity estimation, is constructed to be inverse and optimized problem. We reduce the calculation cost through convex optimization and make the velocity field more accurate by coupling with Navier–Stokes equations. The spatial resolution receives significant enhancement by applying advection on original data with higher‐resolution velocity field data generated by our method. The temporal resolution is improved by generating intermediate velocity fields through the solution of Navier–Stokes equations. In this paper, we demonstrate that the accuracy of our velocity estimation method is clearly better than that of optical flow methods and the enhanced data show an attractive performance in fluid visualization. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: Related Workmentioning

confidence: 99%

Physical‐based spatio‐temporal resolution enhancement of scalar data for fluid visualization

Liqun

Chen

2016

Computer Animation & Virtual

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“…This can be done both locally, relating image intensities to parameters of motion or globally by imposing spatial constraints on neighboring flow locations. In this section we will introduce local data terms, while [26] establishes techniques for imposing physical priors globally. We will show that local gradient based approaches are highly flexible and can be applied to a wide range of applications, extracting additional information than only flow fields from the image data.…”

Section: Extended Optical Flow Modelsmentioning

confidence: 99%

“…Here we will confine ourselves to local approaches. The application of global approaches to fluid flow measurements can be found in [26].…”

Section: Solving the Flow Problemmentioning

confidence: 99%

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Spatiotemporal Image Analysis for Fluid Flow Measurements

Garbe

Kondermann

Jehle

et al. 2009

Notes on Numerical Fluid Mechanics and Multidisciplinary Design

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Abstract. In this chapter, a framework will be presented for measuring and modeling transport processes using novel visualization techniques and extended optical flow techniques for digital image sequence analysis. In this way, parameters besides the 2-D xy velocity components can be extracted concurrently from the acquired 2-D image sequences, such as wall shear rates and momentum transport close to boundaries, diffusion coefficients, and depth z in addition to the z velocity components. Depending on the application, particularly the temporal regularization can be enhanced, leading to stabilization of results and reduction of spatial regularization. This is frequently of high importance for flows close to boundaries. Results from applications will be presented from the fields of environmental and life sciences as well as from engineering.

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“…Image sequences used in these applications describe both compressible and incompressible flows. A variety of methods exist for estimating velocity fields, such as Optical Flow [23] and pressure gradients [38], [54] from time-varying images describing incompressible motion.…”

Section: Introductionmentioning

confidence: 99%

Force estimation and prediction from time-varying density images

Jagannathan

Horn

Ratilal

et al. 2012

The Journal of the Acoustical Society of America

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Abstract-We present methods for estimating forces which drive motion observed in density image sequences. Using these forces, we also present methods for predicting velocity and density evolution. To do this, we formulate and apply a Minimum Energy Flow (MEF) method which is capable of estimating both incompressible and compressible flows from time-varying density images. Both the MEF and force-estimation techniques are applied to experimentally obtained density images, spanning spatial scales from micrometers to several kilometers. Using density image sequences describing cell splitting, for example, we show that cell division is driven by gradients in apparent pressure within a cell. Using density image sequences of fish shoals, we also quantify 1) intershoal dynamics such as coalescence of fish groups over tens of kilometers, 2) fish mass flow between different parts of a large shoal, and 3) the stresses acting on large fish shoals.

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Variational Approaches to Image Fluid Flow Estimation with Physical Priors

Cited by 6 publications

References 18 publications

Physical‐based spatio‐temporal resolution enhancement of scalar data for fluid visualization

Physical‐based spatio‐temporal resolution enhancement of scalar data for fluid visualization

Spatiotemporal Image Analysis for Fluid Flow Measurements

Force estimation and prediction from time-varying density images

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