Abstract
| Original language | English |
|---|---|
| Pages (from-to) | 236-243 |
| Journal | Neurocomputing |
| Volume | 220 |
| Early online date | 29 Sep 2016 |
| DOIs | |
| Publication status | Published - 12 Jan 2017 |
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Video interpolation using optical flow and Laplacian smoothness. / Li, Wenbin; Cosker, Darren.
In: Neurocomputing, Vol. 220, 12.01.2017, p. 236-243.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Video interpolation using optical flow and Laplacian smoothness
AU - Li, Wenbin
AU - Cosker, Darren
PY - 2017/1/12
Y1 - 2017/1/12
N2 - 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.
AB - 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.
UR - http://dx.doi.org/10.1016/j.neucom.2016.04.064
U2 - 10.1016/j.neucom.2016.04.064
DO - 10.1016/j.neucom.2016.04.064
M3 - Article
VL - 220
SP - 236
EP - 243
JO - Neurocomputing
JF - Neurocomputing
SN - 0925-2312
ER -