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.
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Rauch, Ł., Sztangret, Ł., Kusiak, J. (2010). Optimization of Parameters of Feed-Back Pulse Coupled Neural Network Applied to the Segmentation of Material Microstructure Images. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artifical Intelligence and Soft Computing. ICAISC 2010. Lecture Notes in Computer Science(), vol 6114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13232-2_11
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DOI: https://doi.org/10.1007/978-3-642-13232-2_11
Publisher Name: Springer, Berlin, Heidelberg
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