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Proceedings Paper

Efficiently determining transform filter coefficients for image processing by applying distributed genetic algorithms
Author(s): Martin Gilligan; Gary B Lamont; Michael R. Peterson
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Paper Abstract

An important aspect of contemporary military communications in the design of robust image transforms for defense surveillance applications. In particular, efficient yet effective transfer of critical image information is required for decision making. The generic use of wavelets to transform an image is a standard transform approach. However, the resulting bandwidth requirements can be quite high, suggesting that a different bandwidth-limited transform be developed. Thus, our specific use of genetic algorithms (GAs) attempts to replace standard wavelet filter coefficients with an optimized transform filter in order to retain or improve image quality for bandwidth-restricted surveillance applications. To find improved coefficients efficiently, we have developed a software engineered distributed design employing a genetic algorithm (GA) parallel island model on small and large computational clusters with multi-core nodes. The main objective is to determine whether running a distributed GA with multiple islands would either give statistically equivalent results quicker or obtain better results in the same amount of time. In order to compare computational performance with our previous serial results, we evaluate the obtained "optimal" wavelet coefficients on test images from both approaches which results in excellent comparative metric values.

Paper Details

Date Published: 29 April 2009
PDF: 7 pages
Proc. SPIE 7347, Evolutionary and Bio-Inspired Computation: Theory and Applications III, 73470E (29 April 2009); doi: 10.1117/12.822215
Show Author Affiliations
Martin Gilligan, Air Force Institute of Technology (United States)
Gary B Lamont, Air Force Institute of Technology (United States)
Michael R. Peterson, University of Alaska Anchorage (United States)

Published in SPIE Proceedings Vol. 7347:
Evolutionary and Bio-Inspired Computation: Theory and Applications III
Teresa H. O'Donnell; Misty Blowers; Kevin L. Priddy, Editor(s)

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