Share Email Print

Proceedings Paper

Suboptimal MAP estimates using A* and genetic algorithms
Author(s): Allen Himler; Harry Wechsler
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

We address the restoration problem for noisy and degraded signals. Novel algorithms for suboptimal MAP estimates have been developed using the A* and genetic algorithms (GAs). The experiments carried out have shown suboptimal A* (SA*) and suboptimal genetic (SGA*) algorithms to be competitive with dynamic programming (DP) for MAP estimation, and that the use of GAs (in SGA*) provides limited gains over SA*. In terms of restoration quality, the suboptimal approaches yield a solution that on the average is only 5% worse than that provided by DP as the noise and/or signal size increase. Our experiments suggest that for limited amounts of noise (about 10%) suboptimal MAP estimates compare favorably against DP in terms of runtime complexity.

Paper Details

Date Published: 1 February 1992
PDF: 11 pages
Proc. SPIE 1607, Intelligent Robots and Computer Vision X: Algorithms and Techniques, (1 February 1992); doi: 10.1117/12.57044
Show Author Affiliations
Allen Himler, George Mason Univ. (United States)
Harry Wechsler, George Mason Univ. (United States)

Published in SPIE Proceedings Vol. 1607:
Intelligent Robots and Computer Vision X: Algorithms and Techniques
David P. Casasent, Editor(s)

© SPIE. Terms of Use
Back to Top