Supervised learning in hierarchical neural networks for edge enhancement

Abstract

Hierarchical artificial neural networks are designed to enhance edge measurement. The neural network comprises four subnets: the Edge Contour Detection subnet, the Maximum Detection subnet, the Gradient Adjustment subnet, and the Orientation Determination subnet. The interconnections between these subnets are fashioned in a hierarchical manner. In order for the neural network system to perform correctly and accurately, each of the neural subnets must be given suitable weights by learning. The learning is very difficult for the hierarchical neural networks because of the complicated hierarchical structure. In our learning algorithm the modularity is introduced for fast learning and good generalization, based on the analysis of the local concept and the distributed concept represented by the module. The amount of information which the nets need to learn is drastically reduced. Therefore, only a small number of training patterns are required to train the nets and still derive suitable weights for the nets to perform accurately and efficiently. The neural network is simulated on a MIPS M120-S machine running UNIX. For the test images degraded by random noise up to 20%, the true edges are detected and enhanced, the false edges are suppressed, the noise is eliminated, the weak edges are reinforced, and the missing edge elements are interpolated.