Title :
Efficient parallel multigrid relaxation algorithms for Markov random field-based low-level vision applications
Author :
Mémin, E. ; Heitz, F. ; Charot, F.
Author_Institution :
IRISA, Rennes, France
Abstract :
We present a new algorithmic framework which enables making a full use of the large potential of data parallelism available on 2D processor arrays for the implementation of nonlinear multigrid relaxation methods. This framework leads to fast convergence towards quasi-optimal solutions. It is demonstrated on two different low-level vision applications
Keywords :
Markov processes; computer vision; parallel algorithms; 2D processor arrays; Markov random field-based low-level vision; algorithmic framework; data parallelism; fast convergence; parallel multigrid relaxation algorithms; quasi-optimal solutions; Machine vision; Markov processes; Parallel algorithms;
Conference_Titel :
Computer Vision and Pattern Recognition, 1994. Proceedings CVPR '94., 1994 IEEE Computer Society Conference on
Conference_Location :
Seattle, WA
Print_ISBN :
0-8186-5825-8
DOI :
10.1109/CVPR.1994.323786
