Automatic correlation and calibration of noisy sensor readings using elite genetic algorithms

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 tab...

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Published in:Artificial intelligence 1996-07, Vol.84 (1), p.339-354
Main Authors: Brooks, R.R., Iyengar, S.S., Chen, J.
Format: Article
Language:English
Subjects:
Artificial intelligence
Correlation
Genetic algorithms
Image matching
Noise reduction
Sensor fusion
Tabu search
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description 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.
doi_str_mv 10.1016/0004-3702(96)00012-4
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source International Bibliography of the Social Sciences (IBSS); Elsevier
subjects Artificial intelligence
Correlation
Genetic algorithms
Image matching
Noise reduction
Sensor fusion
Tabu search
title Automatic correlation and calibration of noisy sensor readings using elite genetic algorithms
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