Fuzzy neural networks for edge detection

Fuzzy neural networks are designed to detect edges. The research comprises two stages: (1) adaptive fuzzification and (2) detection. The fuzzy neural network consists of three layers of neurons. The first layer is an input layer which is divided into eight groups corresponding to blocks in the input pattern. Hence, the structure information in the input pattern is fully utilized. The second layer in the network is used to measure the certainty of the classification for each block. The output layer provides the final measurement of classification. The proposed fuzzy neural network is trained by typical patterns to enable it to determine the edge elements with eight orientations. Pixels having high edge membership are traced and assembled into one picture. The fuzzification, detection, and tracing algorithm are tested. The fuzzy neural network is simulated on a SUN Sparc station. Comparisons are made with a standard edge detection technique. The proposed method obtains a very good result.