&lt;title&gt;Determining Optical Flow&lt;/title&gt;

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

doi:10.1117/12.965761

Cited by 2,831 publications

(187 citation statements)

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“…This is known as the aperture problem of the optical flow algorithms. Consequently, another set of equations is needed, given by some additional constraints (Horn and Schunck, 1981).…”

Section: Optical Flow Based Image Analysis Methodsmentioning

confidence: 99%

“…Optical flow (Horn and Schunck, 1981) is an estimation of the apparent motion (velocity) of objects within an image sequence. It is closely related to motion estimation.…”

Section: Optical Flow Based Image Analysis Methodsmentioning

confidence: 99%

“…-differential methods (Horn and Schunck, 1981;Lucas and Kanade, 1981;Uras et al, 1988): velocity is computed from spatio-temporal derivatives of image intensities. These techniques offer a good trade-off between robustness under noise and density of the flow fields;…”

Section: Optical Flow Based Image Analysis Methodsmentioning

confidence: 99%

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Velocity Field Computation in Vibrated Granular Media Using an Optical Flow Based Multiscale Image Analysis Method

Debayle¹,

Raihane²,

Belhaoua³

et al. 2011

Image Anal Stereol

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An image analysis method has been developed in order to compute the velocity field of a granular medium (sand grains, mean diameter 600 µm) submitted to different kinds of mechanical stresses. The differential method based on optical flow conservation consists in describing a dense motion field with vectors associated to each pixel. A multiscale, coarse-to-fine, analytical approach through tailor sized windows yields the best compromise between accuracy and robustness of the results, while enabling an acceptable computation time. The corresponding algorithm is presented and its validation discussed through different tests. The results of the validation tests of the proposed approach show that the method is satisfactory when attributing specific values to parameters in association with the size of the image analysis window. An application in the case of vibrated sand has been studied. An instrumented laboratory device provides sinusoidal vibrations and enables external optical observations of sand motion in 3D transparent boxes. At 50 Hz, by increasing the relative acceleration Γ, the onset and development of two convective rolls can be observed. An ultra fast camera records the grain avalanches, and several pairs of images are analysed by the proposed method. The vertical velocity profiles are deduced and allow to precisely quantify the dimensions of the fluidized region as a function of Γ.

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“…This is known as the aperture problem of the optical flow algorithms. Consequently, another set of equations is needed, given by some additional constraints (Horn and Schunck, 1981).…”

Section: Optical Flow Based Image Analysis Methodsmentioning

confidence: 99%

“…Optical flow (Horn and Schunck, 1981) is an estimation of the apparent motion (velocity) of objects within an image sequence. It is closely related to motion estimation.…”

Section: Optical Flow Based Image Analysis Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Velocity Field Computation in Vibrated Granular Media Using an Optical Flow Based Multiscale Image Analysis Method

Debayle¹,

Raihane²,

Belhaoua³

et al. 2011

Image Anal Stereol

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show abstract

“…Introducing in the Horn-Shunck [27] formulation for OF the robust potential, one gets the energy functional:…”

Section: Computation Of the Optical Flowmentioning

confidence: 99%

Efficient half-quadratic regularization with granularity control

Rivera

Marroquín

2003

Image and Vision Computing

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In the last decade, several authors have proposed edge preserving regularization (EPR) methods for solving ill posed problems in early vision. These techniques are based on potentials derived from robust M-Estimators. They are capable of detecting outliers in the data, …nding the signi…cant borders of a noisy image and performing an edge-preserving restoration. These methods, however, have some problems: they are computationally expensive, and often produce solutions which are either too smooth or too granular (with borders around small regions). In this paper we present a new class of potentials that permits separate control of robustness and granularity, producing better results than the classical ones in both scalar and vector-valued images. We also present a new fast, memorylimited minimization algorithm.

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“…This gives rise to (the discrete form of) the well-known motion constraint equation (Horn and Schunck 1981) …”

Section: Establishing Correspondencementioning

confidence: 99%

Direct estimation of affine image deformations using visual front-end operations with automatic scale selection

Lindeberg

Proceedings of IEEE International Conference on Computer Vision

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This article deals with the problem of estimating deformations of brightness patterns using visual front-end operations. Estimating such deformations constitutes an important subtask in several computer vision problems relating to image correspondence and shape estimation. The following subjects are t r eated:The problem of decomposing a ne ow elds into simpler components is analysed in detail. A canonical parametrization is presented b ased on singular value decomposition, which naturally separates the rotationally invariant components of the ow eld from the rotationally variant ones.A novel mechanism is presented for automatic selection of scale levels when estimating local a ne deformations. This mechanism is expressed within a multiscale framework where d i s p arity estimates are c omputed in a hierarchical coarse-to-ne manner and corrected using iterative techniques. Then, deformation estimates are selected f r om the scales that minimize a certain normalized r esidual over scales. Finally, the descriptors so obtained serve as initial data for computing re ned estimates of the local deformations.

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<title>Determining Optical Flow</title>

Cited by 2,831 publications

References 0 publications

Velocity Field Computation in Vibrated Granular Media Using an Optical Flow Based Multiscale Image Analysis Method

Velocity Field Computation in Vibrated Granular Media Using an Optical Flow Based Multiscale Image Analysis Method

Efficient half-quadratic regularization with granularity control

Direct estimation of affine image deformations using visual front-end operations with automatic scale selection

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