Paper Info
Title
Motion estimation using the total variation-local-global optical flow and the structure-texture image decomposition.
Abstract
Motion estimation is currently approximated by the visual displacement field called optical flow. The accuracy of optical flow estimation algorithms has been improving steadily as evidenced by results on the Middlebury optical flow benchmark. Actually, several methods are used to estimate the optical flow, but a good compromise between computational cost and accuracy is hard to achieve. This work presents a combined local-global-total variation CLG-TV approach with structure-texture image decomposition. The combination is used to control the propagation phenomena and to gain robustness against illumination changes, influence of texture on the results and sensitivity to outliers. The resulting method is able to compute larger displacements in a reasonable time.
Year
DOI
Venue
2016
10.1504/IJCAT.2016.073609
IJCAT
Field
DocType
Volume
Computer vision,Displacement field,Outlier,Image processing,Robustness (computer science),Optical flow estimation,Artificial intelligence,Motion estimation,Optical flow,Mathematics,Decomposition
Journal
53
Issue
Citations 
PageRank 
1
0
0.34
References 
Authors
14
2
Name
Order
Citations
PageRank
Insaf Bellamine101.01
Hamid Tairi25717.49