Towards A Neural Net Architecture For Rapid Learning In Machine Vision

Paul T. Hadingham

doi:10.1117/12.969957

1 February 1990 Towards A Neural Net Architecture For Rapid Learning In Machine Vision

Paul T. Hadingham

Proceedings Volume 1197, Automated Inspection and High-Speed Vision Architectures III; (1990) https://doi.org/10.1117/12.969957
Event: 1989 Symposium on Visual Communications, Image Processing, and Intelligent Robotics Systems, 1989, Philadelphia, PA, United States

Abstract

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.

Citation Download Citation

Paul T. Hadingham "Towards A Neural Net Architecture For Rapid Learning In Machine Vision", Proc. SPIE 1197, Automated Inspection and High-Speed Vision Architectures III, (1 February 1990); https://doi.org/10.1117/12.969957

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