Document Type
Article
Publication Date
1-1-1996
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 tabu 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.
Publication Source (Journal or Book title)
Artificial Intelligence
First Page
339
Last Page
354
Recommended Citation
Brooks, R., Iyengar, S., & Chen, J. (1996). Automatic correlation and calibration of noisy sensor readings using elite genetic algorithms. Artificial Intelligence, 84 (1-2), 339-354. https://doi.org/10.1016/0004-3702(96)00012-4