Abstract
In this paper, the edge detection using fuzzy neural network is described. The input features are fuzzy sets and a learning algorithm employs fuzzified delta rule. To increase the efficiency during the training, the varied learning rate and the momentum is applied instead of fixed values. In addition, instead of pixel-based inputs, the texture-based inputs are fed into the fuzzy neural network to facilitate and determine the quality of an edge feature. Experimental results have been tested for the case of both step edges and real world images with noise. The performance of a fuzzy neural network edge detector is compared with the neural network and the traditional techniques such as Sobel, LoG, Gabor function, and relaxation.
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