A generalized neighborhood concept is presented which extends the usual techniques for feature extraction using parameter transforms. Generalized neighborhoods allow operators to use the joint information contained in distant portions of the same feature; i.e. to utilize the long-distance correlation present in the image. The generalized neighborhood techniques, by correlating local information over different portions of the image, produce up to two orders of magnitude improvement in accuracy over conventional techniques. The response also becomes more complicated; false features may be detected due to a peculiar form of correlated noise. A general framework, motivated by connectionist networks, is presented which eliminates this behaviour by introducing competitive processes in the parameter spaces. A novel approach to the generation of lateral inhibition links in the networks is proposed which is consistent with generalized neighborhoods. Experiments are provided that show results on range data. Complex surfaces and 3-D surface-intersection curves are reconstructed from the data.
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