A Hybrid Method for Detection of Edges in Grayscale Images

Abstract

Edge detection is the most fundamental but at the same time most important task in image processing and analysis. In the paper a hybrid approach combin ing Neural Network and Fu zzy log ic based edge detection algorithm is proposed to detect edges in grayscale images. To improve the generalizat ion ability, the neural network is trained on fuzzy inputs rather than crisp inputs. The network consists of three layers, one input layer, one hidden layer and one output layer. Fuzzy membership functions are used to convert neurons of input and hidden layer into fuzzy neurons. So the output of first and second layer is the membership value of the corresponding input in the fuzzy set. The proposed technique provides advantage of both neural networks and fuzzy logic and gives satisfactory results for both noisy and noise free images. The method is compared with Roberts, Prewitt, Sobel and Laplacian of Gaussian and other neural network and fuzzy logic based methods and the experimental results reveal that proposed method gives better edge map considering the problem of false edge detection.