Recognition of interacting features has been a difficult task in many existing feature-recognition systems.the unique topological patterns of isolated features change drastically when they interact.Hence many surface-based methods encounter problems in accommodating these changes in their generic feature defintions.recently,much effort has been concentrated on the volumetric approach.However,many of these systems suffer from a problem of combinatorial explosion as the interaction between features becomes more complex.This paper presents a simple and robust system,in which the interacting features are decomposed into primitive regions using a Kohonen self-organizing feature map (SOFM) multilayer feedforward neural network to recognize the features.Self-organization,competitive learning and the clustering of data are some of the SOFM's attributes,exploited in this work to deal with interacting features.
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