Cluster Analysis by Binary Morphology

Authors:
J.-G. Postaire;R. D. Zhang;C. Lecocq-Botte
Affiliations:
-;-;-
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
1993

Citing 3
Cited 13

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Abstract

An approach to unsupervised pattern classification that is based on the use of mathematical morphology operations is developed. The way a set of multidimensional observations can be represented as a mathematical discrete binary set is shown. Clusters are then detected as well separated subsets by means of binary morphological transformations.