Fuzzy classifier system for edge detection

We propose a fuzzy classifier system (FCS) to find a set of fuzzy rules which can carry out the edge detection. The classifier system of Holland (1985) can evaluate the usefulness of rules represented by classifiers with repeated learning. The FCS makes the classifier system be able to carry out the mapping from continuous inputs to outputs. It is the FCS that applies the method of machine learning to the concept of fuzzy logic. The antecedent and consequent of a classifier is same as a fuzzy rule. In the paper, the FCS is the Michigan style. A single fuzzy if-then rule is coded as an individual. The average gray levels which each group of neighbor pixels has are represented in a fuzzy set. Then it is decided whether a pixel is an edge pixel or not by using fuzzy if-then rules. Depending on the average of the gray levels, a number of fuzzy rules can be activated, and each rule makes the output. These outputs are aggregated and defuzzified to take a new gray value of the pixel. To evaluate this edge detection, we compare the new gray level of a pixel with the gray level obtained by other edge detection methods such as Sobel edge detection. This comparison provides a reinforcement signal for the FCS which is reinforcement learning. Also the FCS employs genetic algorithms to make new rules and modify rules when performance of the system needs to be improved.