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Proceedings Paper

Self-calibration of a noisy multiple-sensor system with genetic algorithms
Author(s): Richard Ree Brooks; S. Sitharama Iyengar; Jianhua Chen
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Paper Abstract

This paper explores an image processing application of optimization techniques which entails interpreting noisy sensor data. The application is a generalization of image correlation; we attempt to find the optimal gruence which matches two overlapping gray-scale images corrupted with noise. Both taboo search and genetic algorithms are used to find the parameters which match the two images. A genetic algorithm approach using an elitist reproduction scheme is found to provide significantly superior results. The presentation includes a graphic presentation of the paths taken by tabu search and genetic algorithms when trying to find the best possible match between two corrupted images.

Paper Details

Date Published: 3 January 1996
PDF: 11 pages
Proc. SPIE 2594, Self-Calibrated Intelligent Optical Sensors and Systems, (3 January 1996); doi: 10.1117/12.229229
Show Author Affiliations
Richard Ree Brooks, Louisiana State Univ. (United States)
S. Sitharama Iyengar, Louisiana State Univ. (United States)
Jianhua Chen, Louisiana State Univ. (United States)


Published in SPIE Proceedings Vol. 2594:
Self-Calibrated Intelligent Optical Sensors and Systems
Anbo Wang, Editor(s)

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