Title :
Cluster analysis by binary morphology
Author :
Postaire, J.-G. ; Zhang, R.D. ; Lecocq-Botte, C.
Author_Institution :
Centre d´´Automatique, Univ. des Sci. et Tech. de Lille Flandres Artois, Villeneuve d´´Ascq, France
fDate :
2/1/1993 12:00:00 AM
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
Keywords :
pattern recognition; set theory; binary morphology; mathematical discrete binary set; mathematical morphology operations; multidimensional observations; unsupervised pattern classification; well separated subsets; Application software; Computer vision; Equations; Graphics; Image processing; Morphology; Notice of Violation; Pattern classification; Shape; Stereo vision;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
