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Proceedings Paper

Benchmarking image fusion system design parameters
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Paper Abstract

A clear and absolute method for discriminating between image fusion algorithm performances is presented. This method can effectively be used to assist in the design and modeling of image fusion systems. Specifically, it is postulated that quantifying human task performance using image fusion should be benchmarked to whether the fusion algorithm, at a minimum, retained the performance benefit achievable by each independent spectral band being fused. The established benchmark would then clearly represent the threshold that a fusion system should surpass to be considered beneficial to a particular task. A genetic algorithm is employed to characterize the fused system parameters using a Matlab® implementation of NVThermIP as the objective function. By setting the problem up as a mixed-integer constraint optimization problem, one can effectively look backwards through the image acquisition process: optimizing fused system parameters by minimizing the difference between modeled task difficulty measure and the benchmark task difficulty measure. The results of an identification perception experiment are presented, where human observers were asked to identify a standard set of military targets, and used to demonstrate the effectiveness of the benchmarking process.

Paper Details

Date Published: 5 June 2013
PDF: 12 pages
Proc. SPIE 8706, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXIV, 87060J (5 June 2013); doi: 10.1117/12.2016473
Show Author Affiliations
Christopher L. Howell, U.S. Army RDECOM CERDEC Night Vision & Electronic Sensors Directorate (United States)

Published in SPIE Proceedings Vol. 8706:
Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXIV
Gerald C. Holst; Keith A. Krapels, Editor(s)

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