Self-calibration of a noisy multiple-sensor system with genetic algorithms

Richard Ree Brooks; S. Sitharama Iyengar; Jianhua Chen

doi:10.1117/12.229229

3 January 1996 Self-calibration of a noisy multiple-sensor system with genetic algorithms

Richard Ree Brooks, S. Sitharama Iyengar, Jianhua Chen

Proceedings Volume 2594, Self-Calibrated Intelligent Optical Sensors and Systems; (1996) https://doi.org/10.1117/12.229229
Event: Photonics East '95, 1995, Philadelphia, PA, United States

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.

Citation Download Citation

Richard Ree Brooks, S. Sitharama Iyengar, and Jianhua Chen "Self-calibration of a noisy multiple-sensor system with genetic algorithms", Proc. SPIE 2594, Self-Calibrated Intelligent Optical Sensors and Systems, (3 January 1996); https://doi.org/10.1117/12.229229

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