Using adaptive genetic algorithms in the design of morphological filters in textural image processing

Wei Li; Veronique Haese-Coat; Joseph Ronsin

doi:10.1117/12.235839

25 March 1996 Using adaptive genetic algorithms in the design of morphological filters in textural image processing

Wei Li, Veronique Haese-Coat, Joseph Ronsin

Proceedings Volume 2662, Nonlinear Image Processing VII; (1996) https://doi.org/10.1117/12.235839
Event: Electronic Imaging: Science and Technology, 1996, San Jose, CA, United States

Abstract

An adaptive GA scheme is adopted for the optimal morphological filter design problem. The adaptive crossover and mutation rate which make the GA avoid premature and at the same time assure convergence of the program are successfully used in optimal morphological filter design procedure. In the string coding step, each string (chromosome) is composed of a structuring element coding chain concatenated with a filter sequence coding chain. In decoding step, each string is divided into 3 chains which then are decoded respectively into one structuring element with a size inferior to 5 by 5 and two concatenating morphological filter operators. The fitness function in GA is based on the mean-square-error (MSE) criterion. In string selection step, a stochastic tournament procedure is used to replace the simple roulette wheel program in order to accelerate the convergence. The final convergence of our algorithm is reached by a two step converging strategy. In presented applications of noise removal from texture images, it is found that with the optimized morphological filter sequences, the obtained MSE values are smaller than those using corresponding non-adaptive morphological filters, and the optimized shapes and orientations of structuring elements take approximately the same shapes and orientations as those of the image textons.

Citation Download Citation

Wei Li, Veronique Haese-Coat, and Joseph Ronsin "Using adaptive genetic algorithms in the design of morphological filters in textural image processing", Proc. SPIE 2662, Nonlinear Image Processing VII, (25 March 1996); https://doi.org/10.1117/12.235839

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