TV-L1 Optical Flow Estimation

Sánchez, Javier; Meinhardt-Llopis, Enric; Facciolo, Gabriele

doi:10.5201/ipol.2013.26

Cited by 335 publications

(153 citation statements)

References 8 publications

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“…Both are based on partial derivatives of the image signal or the sought flow field and higher-order partial derivatives. The most common algorithms for meteorological purposes concerning optical flow are by Farnebäck [26] and the duality based approach of the method TV-L 1 [27,28].…”

Section: Optical Flow Methodsmentioning

confidence: 99%

“…Like the Farnebäck method, TV-L 1 uses two frames to estimate the optical flow. The TV-L 1 method is based on the minimization of a functional containing a data term using the robust L 1 -norm in the data fidelity term and a regularization term using the total variation (TV) of the flow [27]. The method is based on the minimization of the following image-based error criterion:…”

Section: Optical Flow Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Approach for the Short-Term Forecast of the Effective Cloud Albedo

Urbich¹,

Bendix²,

Müller³

2018

Preprint

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Abstract:The increasing use of renewable energies as a source of electricity has led to a fundamental transition of the power supply system. The integration of fluctuating weather-dependent energy sources into the grid already has a major impact on the load flows of the grid. As a result, the interest in forecasting wind and solar radiation with a sufficient accuracy over short time horizons grew. In this study the short-term forecast of the effective cloud albedo based on optical flow estimation methods are investigated. The optical flow method utilized here is TV-L 1 from the open source library OpenCV. This method uses a multi-scale-approach to capture cloud motions on various spatial scales. After the clouds are displaced the solar surface radiation will be calculated with SPECMAGIC NOW which computes the global irradiation spectrally resolved from satellite imagery. Due to a high temporal and spatial resolution of satellite measurements the effective cloud albedo and thus solar radiation can be forecasted from 5 minutes up to 4 hours with a resolution of 0.05 • . In the following there will be a brief description of the method for the short-term forecast of the effective cloud albedo. Subsequently evaluation results will be presented and discussed. Finally an outlook of further developments will be given.

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Section: Optical Flow Methodsmentioning

confidence: 99%

Section: Optical Flow Methodsmentioning

confidence: 99%

A Novel Approach for the Short-Term Forecast of the Effective Cloud Albedo

Urbich¹,

Bendix²,

Müller³

2018

Preprint

View full text Add to dashboard Cite

show abstract

“…The generalization in the use of L 1 functionals was proposed in several works, such as in Brox et al [6] and Zach et al [18]. These two methods have been analyzed in the IPOL articles by Sánchez et al, [15] and [16], respectively. These strategies produce piecewise smooth motion fields but, since they do not use any image information in the regularization process, their flow edges do not usually coincide with the object boundaries.…”

Section: Introductionmentioning

confidence: 99%

Robust Discontinuity Preserving Optical Flow Methods

Monzón¹,

Salgado²,

Sánchez³

2016

Image Processing on Line

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In this work, we present an implementation of discontinuity-preserving strategies in TV-L 1 optical flow methods. These are based on exponential functions that mitigate the regularization at image edges, which usually provide precise flow boundaries. Nevertheless, if the smoothing is not well controlled, it may produce instabilities in the computed motion fields. We present an algorithm that allows three regularization strategies: the first one uses an exponential function together with a TV process; the second one combines this strategy with a small constant that ensures a minimum isotropic smoothing; the third one is a fully automatic approach that adapts the diffusion depending on the histogram of the image gradients. The last two alternatives are aimed at reducing the effect of instabilities. In the experiments, we observe that the pure exponential function is highly unstable while the other strategies preserve accurate motion contours for a large range of parameters. Source CodeThe source code, its documentation and the online demo are accessible at the IPOL web page of this article 1 . In this page an implementation is available for download.

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“…We use the method described in [18] for this computation, and the rest of its parameters are set to the default values proposed in this article. This parameter determines the smoothness of the obtained flow, the smaller the parameter the smoother the result.…”

Section: Patches (Undo Centering)mentioning

confidence: 99%

Video Denoising with Optical Flow Estimation

Buades¹,

Lisani²

2018

Image Processing on Line

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In this paper we describe the implementation of state-of-the-art video denoising algorithm SPTWO [A. Buades, J.L. Lisani, M. Miladinović, Patch Based Video Denoising with Optical Flow Estimation, IEEE Transactions on Image Processing 25 (6), [2573][2574][2575][2576][2577][2578][2579][2580][2581][2582][2583][2584][2585][2586]. This algorithm, inspired by image fusion techniques, uses motion compensation by regularized optical flow methods, which permits robust patch comparison in spatiotemporal volumes. Groups of similar patches are denoised using Principal Component Analysis, which ensures the correct preservation of fine texture and details. Source CodeThe reviewed source code and documentation for this algorithm are available from the web page of this article 1 .

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TV-L1 Optical Flow Estimation

Cited by 335 publications

References 8 publications

A Novel Approach for the Short-Term Forecast of the Effective Cloud Albedo

A Novel Approach for the Short-Term Forecast of the Effective Cloud Albedo

Robust Discontinuity Preserving Optical Flow Methods

Video Denoising with Optical Flow Estimation

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