Artificial neural networks have been shown to perform well in many image processing applications such as coding, pattern recognition and texture segmentation. In a typical multi-layer model of this class, neurons in each layer are linked by synaptic weights to a receptive field region in the layer below it. The input image itself is linked to the lowest layer. We propose here a two stage encoder-detector network for edge detection. The single layer encoder stage, trained in a competitive mode, compresses data from an input receptive field and drives a back-propagation-trained detector network whose two outputs represent components of an edge vector. Experimental results show that for the case of step edges in noisy images, the performance of the neural edge detector is comparable to that of the Canny detector. © 1994.
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