DocumentCode :
3333485
Title :
Optical Flow Estimation Using Laplacian Mesh Energy
Author :
Wenbin Li ; Cosker, D. ; Brown, Michael ; Rui Tang
Author_Institution :
Dept. of Comput. Sci., Univ. of Bath, Bath, UK
fYear :
2013
fDate :
23-28 June 2013
Firstpage :
2435
Lastpage :
2442
Abstract :
In this paper we present a novel non-rigid optical flow algorithm for dense image correspondence and non-rigid registration. The algorithm uses a unique Laplacian Mesh Energy term to encourage local smoothness whilst simultaneously preserving non-rigid deformation. Laplacian deformation approaches have become popular in graphics research as they enable mesh deformations to preserve local surface shape. In this work we propose a novel Laplacian Mesh Energy formula to ensure such sensible local deformations between image pairs. We express this wholly within the optical flow optimization, and show its application in a novel coarse-to-fine pyramidal approach. Our algorithm achieves the state-of-the-art performance in all trials on the Garg et al. dataset, and top tier performance on the Middlebury evaluation.
Keywords :
computer graphics; computer vision; image registration; image sequences; mesh generation; shape recognition; Laplacian mesh energy; Middlebury evaluation; coarse-to-fine pyramidal approach; computer vision research; dense image correspondence; graphics research; image pairs; local smoothness; local surface shape preservation; nonrigid deformation preservation; nonrigid optical flow algorithm; nonrigid registration; optical flow estimation; optical flow optimization; Computer vision; Estimation; Image edge detection; Laplace equations; Optical imaging; Optimization; Vectors; Laplacian Mesh; Optical Flow;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on
Conference_Location :
Portland, OR
ISSN :
1063-6919
Type :
conf
DOI :
10.1109/CVPR.2013.315
Filename :
6619159
Link To Document :
بازگشت