Texture classification by gray-scale morphological granulometries

Yidong Chen, Edward R. Dougherty

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
PublisherPubl by Int Soc for Optical Engineering
Pages931-942
Number of pages12
Editionpt 2
ISBN (Print)0819410187
StatePublished - 1992
Externally publishedYes
EventVisual Communications and Image Processing '92 - Boston, MA, USA
Duration: Nov 18 1992Nov 20 1992

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Numberpt 2
Volume1818
ISSN (Print)0277-786X

Other

OtherVisual Communications and Image Processing '92
CityBoston, MA, USA
Period11/18/9211/20/92

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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