Towards A Neural Net Architecture For Rapid Learning In Machine Vision
Paul T. Hadingham · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1990
Though they boast many attractive features, major problems exist in using an artificial neural network to represent knowledge. In particular, network training is usually a considerable task. Moreover, the network connections in the trained network bear no obviously derivable relationship to the component structures or concepts existing in the knowledge which has been learned. Research described here attempts to tackle both these issues in the context of edge interpretation in machine vision. The basis for a novel artificial neural network architecture is proposed which supports the direct representation of domain knowledge, in this case, the relationship between simple edge patterns and objects represented by the edge patterns. This means that the time-consuming training cycle can be avoided because network weights are directly calculated as functions of the edge structures which make up each object so that connection weights have a natural interpretation in terms of concepts comprising an object. The notions of arc and arc relation space have been developed as the cornerstones of this architecture. Their analysis in this limited domain indicates that such spaces may have a significant part to play in the general context of object recognition based on edge structures in machine vision.