Paper Info
Title
Motion estimation using the fast and adaptive bidimensional empirical mode decomposition
Abstract
Motion estimation is a basic step that can be used to serve several processes in computer vision. This motion is currently approximated by the visual displacement field called optical flow. Currently, several methods are used to estimate it, but a good compromise between computational cost and accuracy is hard to achieve. This paper tackles the problem by proposing a new technique based on the FABEMD (fast and adaptive bidimensional empirical mode decomposition) with the aim of improving the well-known pyramidal algorithm of Lucas and Kanade (LK) which, in principle, utilizes two consecutive frames extracted from video sequence to determine a dense optical flow. The proposed algorithm uses the FABEMD method to decompose each of the two considered frames into several BIMFs (bidimensional intrinsic mode functions) that are matched in number and proprieties. Thus, to compute the optical flow, the LK algorithm is applied to each of the two matching BIMFs which belong to the same mode of the decomposition. Although the implementation does not use an iterative refinement, the results show that the proposed approach is less sensitive to noise and provides improved motion estimation with a reduction of computing time compared to iterative methods.
Year
DOI
Venue
2014
10.1007/s11554-012-0259-4
Journal of Real-Time Image Processing
Keywords
Field
DocType
bimf,motion estimation,fabemd,pyramidal implementation,optical flow
Iterative refinement,Computer vision,Displacement field,Computer science,Iterative method,Artificial intelligence,Motion estimation,Optical flow,Hilbert–Huang transform
Journal
Volume
Issue
ISSN
9
3
1861-8219
Citations 
PageRank 
References 
6
0.45
16
Authors
3
Name
Order
Citations
PageRank
M. A. Mahraz160.79
J. Riffi260.45
Hamid Tairi35717.49