Morphological Feature Detection

Abstract

We describe investigations applying grey-scale mathematical morphology to the problem of feature detection. We show how a combination of morphological operators can be interpreted in terms of the differential geometrical characteristics of the intensity surface. This is significant in that it provides insight into how morphological operators manipulate image data in a manner that has no parallel in traditional convolutionbased image processing. Results using a simple morphological boundary detector compare favourably with the output of a normal edge detector 3uch as the Canny operator. However, boundary detection differs in two important respects; the performance is generally better in regions of high image curvature and image junction information remains explicit. We provide experimental evidence to support these claims. An image description is only of use if it is an aid to image understanding. We conclude with a brief discussion of a morphologically derived scheme based on boundary surface features and indicate how such a description provides potentially powerful constraints for correspondence algorithms.