Macroscopic Rock Texture Image Classification Using an Hierarchical Neuro-Fuzzy System

Author

Gonçalves, Laercio B. ; Leta, Fabiana R. ; de Valente, S.C.

fYear

2009

fDate

18-20 June 2009

Firstpage

1

Lastpage

5

Abstract

This paper explores the use of an hierarchical neurofuzzy model for image classification of macroscopic rock texture. The relevance of this study is to help geologists in diagnosing and planning the oil reservoir exploitation. The same approach can be also applied to metals, in order to classify the different types of materials based on their grain texture. We present an image classification for macroscopic rocks, based on these texture descriptors and on a neuro-fuzzy approach.

Keywords

fuzzy logic; geophysical prospecting; geophysical signal processing; hydrocarbon reservoirs; image classification; neural nets; rocks; surface texture; hierarchical neuro fuzzy system; macroscopic rock texture image classification; material grain texture classification; oil reservoir exploitation; texture descriptors; Fuzzy neural networks; Geology; Hydrocarbon reservoirs; Image classification; Image color analysis; Image texture analysis; Inorganic materials; Mechanical engineering; Optical microscopy; Petroleum;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Signals and Image Processing, 2009. IWSSIP 2009. 16th International Conference on

Conference_Location

Chalkida

Print_ISBN

978-1-4244-4530-1

Electronic_ISBN

978-1-4244-4530-1

Type

conf

DOI

10.1109/IWSSIP.2009.5367745

Filename

5367745