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Proceedings Paper

The role of wavelet coefficients in fitness landscapes of image transforms for defense applications
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Paper Abstract

Evolutionary algorithms (EAs) have been employed in recent years in the design of robust image transforms. EAs attempt to improve the defining filter coefficients of a discrete wavelet transform (DWT) to improve image quality for bandwidth-restricted surveillance applications, such as the transmission of images by swarms of unmanned aerial vehicles (UAVs) over shared channels. Regardless of the specific algorithm employed, filter coefficients are optimized over a common fitness landscape that defines allowable configurations that filters may take. Any optimization algorithm attempts to identify highly-fit filter configurations within the landscape. The evolvability of transform filters depends upon the ruggedness, deceptiveness, neutrality, and modality of the underlying landscape traversed by the EA. We have previously studied the evolvability of image transforms for satellite image processing with regards to ruggedness and deceptiveness. Here we examine the position of wavelet coefficients within a landscape to determine whether optimization algorithms should be seeded near this position or randomly seeded in the global landscape. Through examination of landscape deceptiveness, both near wavelet coefficients and throughout the global range of the landscape, we determine that the neighborhood surrounding the wavelet contains a greater concentration of highly fit solutions. EAs that concentrate their search effort in this neighborhood have a better chance of identifying filters that improve upon standard wavelets. An improved understanding of the underlying fitness landscape characteristics impacts the design of evolutionary algorithms capable of identifying near-optimal image transforms suitable for deployment in defense and security applications of image processing.

Paper Details

Date Published: 29 April 2009
PDF: 11 pages
Proc. SPIE 7347, Evolutionary and Bio-Inspired Computation: Theory and Applications III, 73470D (29 April 2009); doi: 10.1117/12.820296
Show Author Affiliations
Michael R. Peterson, Univ. of Alaska Anchorage (United States)
Gary B. Lamont, Air Force Institute of Technology (United States)
Frank Moore, Univ. 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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