Iterative segmentation algorithms using morphological operations

Authors:
Patrick A. Kelly;Feng Chen
Affiliations:
Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA;Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA
Venue:
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: image and multidimensional signal processing - Volume V
Year:
1993

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Abstract

In this paper we describe some iterative segmentation algorithms that combine statistical constraints represented in Markov Random Field models with deterministic constraints imposed by morphological operations. The goal is to produce segmentations that have high probability according to the Markov model and are smooth in the sense of being morphologically open and/or closed. We first present several algorithms for binary images, including one that produces a segmentation in which the set of 1's is both open and closed. The latter algorithm is then extended to the case of multi-region images to produce a segmentation in which each region is open and closed.