Optimization of parameters of feed-back pulse coupled neural network applied to the segmentation of material microstructure images

Authors:
Łukasz Rauch;Łukasz Sztangret;Jan Kusiak
Affiliations:
AGH-University of Science and Technology, Krakow, Poland;AGH-University of Science and Technology, Krakow, Poland;AGH-University of Science and Technology, Krakow, Poland
Venue:
ICAISC'10 Proceedings of the 10th international conference on Artifical intelligence and soft computing: Part II
Year:
2010

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Abstract

The paper presents application of bio-inspired optimization procedures to the problem of image segmentation of material microstructures. The method used for image processing was Feed-Back Pulse Coupled Neural Network (FBPCNN), which is very flexible in the case of highly diversified images, offering interesting results of segmentation. However, six input parameters of FBPCNN have to be adjusted dependently on image content to obtain optimal results. This was the main objective of the paper. Therefore, the procedure of image segmentation assessment was proposed on the basis of number of segments, their size, entropy and fractal dimension. The proposed evaluation was used as objective function in optimization algorithms. The results obtained for Simple Genetic Algorithms, Particle Swarm Optimization and Simulated Annealing are presented.