Name
Title | ||
|---|---|---|
Efficient parallel multigrid relaxation algorithms for Markov random field-based low-level vision applications |
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 |
Year | DOI | Venue |
|---|---|---|
1994 | 10.1109/CVPR.1994.323786 | Seattle, WA |
Keywords | Field | DocType |
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 | Markov process,Computer science,Markov random field,Theoretical computer science,Computational science,Artificial intelligence,Markov algorithm,Computer vision,Markov model,Parallel algorithm,Relaxation (iterative method),Data parallelism,Multigrid method | Conference |
Volume | Issue | ISSN |
1994 | 1 | 1063-6919 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Étienne Mémin | 1 | 270 | 26.44 |
| Fabrice Heitz | 2 | 401 | 59.55 |
| François Charot | 3 | 136 | 15.93 |