A neural network for texture classification

Author

Branca, A. ; Tafuri, M. ; Attolico, G. ; Distante, A.

Author_Institution

Istituto Elaborazione Segnali ed Immagini, CNR, Bari, Italy

Volume

2

fYear

1996

fDate

13-16 May 1996

Firstpage

653

Abstract

This paper describes a new method, based on recovery of directionality from compositional textures. The technique uses features obtained as projection coefficients of the flow field estimated from texture onto suitable elementary basis vector fields. A neural network is used for both the estimation of the best projection into the feature space and the segmentation of the original image into homogeneous regions. The technique has been verified on both natural and artificial textures; some results obtained on leather for defects detection and classification in manufacturing industry are shown

Keywords

image classification; image segmentation; image texture; neural nets; classification; compositional textures; defects detection; directionality; elementary basis vector fields; feature space; homogeneous regions; leather; manufacturing industry; neural network; projection coefficients; segmentation; texture classification; Anisotropic magnetoresistance; Artificial neural networks; Fluid flow measurement; Image retrieval; Inspection; Manufacturing industries; Neural networks; Taxonomy; Testing; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrotechnical Conference, 1996. MELECON '96., 8th Mediterranean

Conference_Location

Bari

Print_ISBN

0-7803-3109-5

Type

conf

DOI

10.1109/MELCON.1996.551304

Filename

551304