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Video interpolation using optical flow and Laplacian smoothness


Reference:

Li, W. and Cosker, D., 2017. Video interpolation using optical flow and Laplacian smoothness. Neurocomputing, 220, pp. 236-243.

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Official URL:

http://dx.doi.org/10.1016/j.neucom.2016.04.064

Abstract

Non-rigid video interpolation is a common computer vision task. In this paper we present an optical flow approach which adopts a Laplacian Cotangent Mesh constraint to enhance the local smoothness. Similar to Li et al., our approach adopts a mesh to the image with a resolution up to one vertex per pixel and uses angle constraints to ensure sensible local deformations between image pairs. The Laplacian Mesh constraints are expressed wholly inside the optical flow optimization, and can be applied in a straightforward manner to a wide range of image tracking and registration problems. We evaluate our approach by testing on several benchmark datasets, including the Middlebury and Garg et al. datasets. In addition, we show application of our method for constructing 3D Morphable Facial Models from dynamic 3D data.

Details

Item Type Articles
CreatorsLi, W.and Cosker, D.
DOI10.1016/j.neucom.2016.04.064
DepartmentsFaculty of Science > Computer Science
Research CentresMedia Technology Research Centre
EPSRC Centre for Doctoral Training in Statistical Mathematics (SAMBa)
RefereedYes
StatusPublished
ID Code49975

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