AUTOMATIC CORRELATION AND CALIBRATION OF NOISY SENSOR READINGS USING ELITE GENETIC ALGORITHMS

This paper explores an image processing application of optimization te chniques which entails interpreting noisy sensor data. The application is a generalization of image correlation; we attempt to find the opti mal gruence which matches two overlapping gray scale images corrupted with noise. Both tabu search and genetic algorithms are used to find t he parameters which match the two images. A genetic algorithm approach using an elitist reproduction scheme is found to provide significantl y superior results.