Gray-scale morphological granulometric texture classification

Yidong Chen, Edward R. Dougherty

Research output: Contribution to journalArticlepeer-review

86 Scopus citations


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 extension of the local-classification technique to gray- scale textures is investigated. It does so by using 42 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. Sequential feature selection is also applied. The effect 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.

Original languageEnglish (US)
Pages (from-to)2713-2722
Number of pages10
JournalOptical Engineering
Issue number8
StatePublished - 1994
Externally publishedYes

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Engineering(all)


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