Smart Feature Detection using an Invariance Network Architecture.

The use of image features greatly improves the computational efficiency of subsequent machine vision tasks by directing processing effort to information rich areas of the image. However, the extraction of image features is an intensive process where the usual mapping between image data and feature is only approximately defined. Further, feature enhanced images provide very little information regarding the reliability of the information they embody. This results from the application of heuristics during the later stage of feature extraction. This paper describes an artificial neural network trained to perform feature detection. The network is 'tuned' to extract a particular feature type. This is achieved using the functional mapping of an existing detection algorithm once systematic errors in this technique were removed. The scale of the mapping problem is reduced by enhancing the invariant characteristics of the feature. Only then is a manageable sized network able to perform the mapping task. The network enables the conditional probability of a corner to be used in the final image segmentation.