Evolving Cellular Automata for Two-Stage Edge Detection

This paper presents an edge detection method based on Cellular Automata where the rules are evolved to optimize the edge detection in binary images. This method divides the edge detection problem into two sub–problems: on the one hand it trains the rules to detect the edge pixels, on the other hand it trains the rules to detect non–edge (background) pixels. Two best packets of rules are obtained from the training process. These packets of rules are applied in different orders or after they have been processed, thus resulting four different images on which the detection performance of the proposed method is evaluated.

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