Multiresolution edge labelling using hierarchical relaxation

The authors present a hierarchical extension of the probabilistic relaxation method and demonstrate its application to the multiscale processing of edge information. The basic idea is to utilise interlevel constraints on the evolution of edge structure to locate consistent edge labellings at each descending level of a resolution pyramid. Information concerning the whereabouts of consistent edge structure is passed from one layer of the pyramid to another in the form of a labelled edge map. This information is combined with the raw edge data at the relevant level of the pyramid using a Bayesian extension of the probabilistic relaxation formula of A. Rosenfeld, R.A. Hummel and S.W. Zucker; the result is a fine resolution label interpretation. Since the consistent edge structures permitted at coarse resolution have a much greater spatial extent than those permitted at fine resolution, they provide more powerful constraints on edge connectivity. This means that noise contamination can be efficiently controlled without the use of excessive filtering and consequent band limitation of genuine high frequency image structure