
Proceedings Paper
Fitness landscape analysis of evolved image transforms for defense applicationsFormat | Member Price | Non-Member Price |
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Paper Abstract
In recent years, there has been increased interest in the use of evolutionary algorithms (EAs) in the design of robust
image transforms for use in defense and security applications. An EA replaces the defining filter coeffcients
of a discrete wavelet transform (DWT) to provide improved image quality within bandwidth-limited image processing
applications, such as the transmission of surveillance data by swarms of unmanned aerial vehicles (UAVs)
over shared communication channels. The evolvability of image transform filters depends upon the properties
of the underlying fitness landscape traversed by the evolutionary algorithm. The landscape topography determines
the ease with which an optimization algorithm may identify highly-fit filters. The properties of a fitness
landscape depend upon a chosen evaluation function defined over the space of possible solutions. Evaluation
functions appropriate for image filter evolution include mean squared error (MSE), the universal image quality
index (UQI), peak signal-to-noise ratio (PSNR), and average absolute pixel error (AAPE). We conduct a theoretical
comparison of these image quality measures using random walks through fitness landscapes defined over
each evaluation function. This analysis allows us to compare the relative evolvability of the various potential
image quality measures by examining fitness topology for each measure in terms of ruggedness and deceptiveness.
A theoretical understanding of the topology of fitness landscapes aids in 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: 1 May 2008
PDF: 12 pages
Proc. SPIE 6964, Evolutionary and Bio-Inspired Computation: Theory and Applications II, 69640H (1 May 2008); doi: 10.1117/12.777286
Published in SPIE Proceedings Vol. 6964:
Evolutionary and Bio-Inspired Computation: Theory and Applications II
Misty Blowers; Alex F. Sisti, Editor(s)
PDF: 12 pages
Proc. SPIE 6964, Evolutionary and Bio-Inspired Computation: Theory and Applications II, 69640H (1 May 2008); doi: 10.1117/12.777286
Show Author Affiliations
Michael R. Peterson, Wright State Univ. (United States)
Gary B. Lamont, Air Force Institute of Technology (United States)
Published in SPIE Proceedings Vol. 6964:
Evolutionary and Bio-Inspired Computation: Theory and Applications II
Misty Blowers; Alex F. Sisti, Editor(s)
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