3 January 1996 Self-calibration of a noisy multiple-sensor system with genetic algorithms
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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.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Richard Ree Brooks, Richard Ree Brooks, S. Sitharama Iyengar, S. Sitharama Iyengar, Jianhua Chen, 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); doi: 10.1117/12.229229; https://doi.org/10.1117/12.229229


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