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Proceedings Paper

Texture classification by gray-scale morphological granulometries
Author(s): Yidong Chen; Edward R. Dougherty
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Paper Abstract

Binary morphological granulometric size distributions were conceived by Matheron as a way of describing image granularity (or texture). Since each normalized size distribution is a probability density, feature vectors of granulometric moments result. Recent application has focused on taking local size distributions around individual pixels so that the latter can be classified by surrounding texture. The present paper investigates the extension of the local- classification technique to gray-scale textures. It does so by using forty-two granulometric features, half generated by opening granulometries and a dual half generated by closing granulometries. After training and classification of both dependent and independent data, feature extraction (compression) is accomplished by means of the Karhunen-Loeve transform. The effect of randomly placed Gaussian noise is investigated.

Paper Details

Date Published: 1 November 1992
PDF: 12 pages
Proc. SPIE 1818, Visual Communications and Image Processing '92, (1 November 1992); doi: 10.1117/12.131505
Show Author Affiliations
Yidong Chen, Rochester Institute of Technology (United States)
Edward R. Dougherty, Rochester Institute of Technology (United States)


Published in SPIE Proceedings Vol. 1818:
Visual Communications and Image Processing '92
Petros Maragos, Editor(s)

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