Determining optical flow

Horn, Berthold K. P.; Schunck, Brian G.

doi:10.1016/0004-3702(81)90024-2

Cited by 9,271 publications

(4,772 citation statements)

References 12 publications

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“…The second approach focuses on the penalization of rapid changes in the velocity field and has a similar effect as the regularization functional in the Horn and Schunck algorithm [7]. For each velocity vector u = (u x (t, i, j), u y (t, i, j)), we add the linear equations of the form β•(grad u x ) = 0 and β•(grad u y ) = 0 where we use Eq.…”

Section: Flow Image Velocimetry Methods Based On Advection-diffusion Ementioning

confidence: 99%

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Flow Image Velocimetry Method Based on Advection-Diffusion Equation

Bizjan

Orbanić

Širok

et al. 2014

SV-JME

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Section: Flow Image Velocimetry Methods Based On Advection-diffusion Ementioning

confidence: 99%

“…Horn and Schunck [7] developed an early optical flow calculation method based on image brightness analysis and an assumption of smooth velocity variations using a quadratic penalization scheme. Through the years, optical flow computation methods have been improved significantly in both accuracy and robustness.…”

Section: Introductionmentioning

confidence: 99%

Flow Image Velocimetry Method Based on Advection-Diffusion Equation

Bizjan

Orbanić

Širok

et al. 2014

SV-JME

View full text Add to dashboard Cite

“…To determine the optical flow, a variational formulation with a smoothness constraint is typically used [7,13]. Given g and f, we define a functional:…”

Section: B Variational Solutionmentioning

confidence: 99%

“…In the special case where g∇·u = 0 and f = 0, Eq. (1) is reduced to the Horn-Schunck brightness constraint equation ∂g/∂t + u·∇g = 0 [13]. In general, the optical flow is not divergence-free, i.e., ∇·u ≠ 0.…”

Section: Introductionmentioning

confidence: 99%

OpenOpticalFlow: An Open Source Program for Extraction of Velocity Fields from Flow Visualization Images

Liu

2017

JORS

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“…Optical flow estimation, as a hot topic, has been extensively studied [5]. Most of the existing approaches are based on variational formulations and the related energy minimization.…”

Section: Related Workmentioning

confidence: 99%

Light Field Flow Estimation Based on Occlusion Detection

Zhang¹,

Lin²

2017

JCC

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Light field cameras have a wide area of applications, such as digital refocusing, scene depth information extraction and 3-D image reconstruction. By recording the energy and direction information of light field, they can well solve many technical problems that cannot be done by conventional cameras. An important feature of light field cameras is that a microlens array is inserted between the sensor and main lens, through which a series of sub-aperture images of different perspectives are formed. Based on this feature and the full-focus image acquisition technique, we propose a light-field optical flow calculation algorithm, which involves both the depth estimation and the occlusion detection and guarantees the edge-preserving property. This algorithm consists of three steps: 1) Computing the dense optical flow field among a group of sub-aperture images; 2) Obtaining a robust depth-estimation by initializing the light-filed optical flow using the linear regression approach and detecting occluded areas using the consistency; 3) Computing an improved light-field depth map by using the edge-preserving algorithm to realize interpolation optimization. The reliability and high accuracy of the proposed approach is validated by experimental results.

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Determining optical flow

Cited by 9,271 publications

References 12 publications

Flow Image Velocimetry Method Based on Advection-Diffusion Equation

Flow Image Velocimetry Method Based on Advection-Diffusion Equation

OpenOpticalFlow: An Open Source Program for Extraction of Velocity Fields from Flow Visualization Images

Light Field Flow Estimation Based on Occlusion Detection

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