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Cluster analysis by binary morphology

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3 Author(s)
J. -G. Postaire ; Centre d'Automatique, Univ. des Sci. et Tech. de Lille Flandres Artois, Villeneuve d'Ascq, France ; R. D. Zhang ; C. Lecocq-Botte

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

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:15 ,  Issue: 2 )