EDGE-DETECTION USING A NEURAL-NETWORK

Artificial neural networks have been shown to perform well in many ima ge processing applications such as coding, pattern recognition and tex ture segmentation. In a typical multi-layer model of this class, neuro ns in each layer are linked by synaptic weights to a receptive field r egion in the layer below it. The input image itself is linked to the l owest layer. We propose here a two stage encoder-detector network for edge detection. The single layer encoder stage, trained in a competiti ve mode, compresses data from an input receptive field and drives a ba ck-propagation-trained detector network whose two outputs represent co mponents of an edge vector. Experimental results show that for the cas e of step edges in noisy images, the performance of the neural edge de tector is comparable to that of the Canny detector.