Abstract
Cellular Automata (CA) can be successfully applied in various image processing tasks because they have a number of advantages over the traditional methods of computations: simplicity of implementation, the complexity of behaviour, parallelisation, extensibility, scalability, robustness. In this paper, an edge detection method for binary images, based on CA and Evolutionary Algorithms (EA) is presented. The rule of a two-dimensional CA is evolved by the means of two EAs, one that evolves the rule to detect edge points and another one that evolves the rule to detect non-edge points. The focus is on the implementation of the EAs, how individuals are represented, how the evolving process is evaluated and which genetic operators are used. The results of the experiments show a better performance of the proposed approach in comparison with similar approaches presented in the literature.
Citare
A. Enescu, A. Andreica, L. Diosan, Evolved cellular automata for edge detection., Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 316-317.