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Titolo:
GRAY-SCALE MORPHOLOGICAL GRANULOMETRIC TEXTURE CLASSIFICATION
Autore:
CHEN YD; DOUGHERTY ER;
Indirizzi:
ROCHESTER INST TECHNOL,CTR IMAGING SCI,54 LOMB MEM DR ROCHESTER NY 14623
Titolo Testata:
Optical engineering
fascicolo: 8, volume: 33, anno: 1994,
pagine: 2713 - 2722
SICI:
0091-3286(1994)33:8<2713:GMGTC>2.0.ZU;2-L
Fonte:
ISI
Lingua:
ENG
Keywords:
MATHEMATICAL MORPHOLOGY; TEXTURE; GRANULOMETRY; PATTERN SPECTRUM; MAXIMUM-LIKELIHOOD CLASSIFICATION;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Science Citation Index Expanded
Citazioni:
9
Recensione:
Indirizzi per estratti:
Citazione:
Y.D. Chen e E.R. Dougherty, "GRAY-SCALE MORPHOLOGICAL GRANULOMETRIC TEXTURE CLASSIFICATION", Optical engineering, 33(8), 1994, pp. 2713-2722

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, featurevectors of granulometric moments result. Recent application has focused on taking local size distributions around individual pixels so thatthe latter can be classified by surrounding texture. The extension ofthe local-classification technique to gray-scale textures is investigated. It does so by using 42 granulometric features, half generated byopening 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. Sequential feature selection is also applied. The eff ect of randomly placed uniform noise is investigated. In particular, the degree to which training in noise increases robustness across noise levels is studied, and feature selection is employed to arrive at a noise-insensitive set of granulometric classifiers.

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Documento generato il 24/09/20 alle ore 01:22:45