Share Email Print
cover

Proceedings Paper

Generalized and optimized classification framework for textural imagery
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

A wide range of image processing studies are based on the extraction of texture features, the analysis of input data and the identification and design of appropriate classifiers given a particular application, for instance, in the fields of industrial inspection, remote sensing, medicine or biology amongst others. In this paper, we introduce a novel generalized classification framework for texture imagery based on a novel building blocks system architecture and present the advantages of such a system to tackle a variety of image analysis problems at the same time of obtaining good classification performances. Firstly, an overview of the system architecture is described from the texture feature extraction module to the data analysis and the classification building blocks. Thus, we obtain an optimized and generic classification framework which is highly flexible due to its scalable building blocks system approach and provides the facility to extend easily the study obtained for textural images to other kind of imagery. The results of this generalized classification framework are validated using imagery from two different application fields where texture plays a key role. The first one is in the field of remote sensing for agriculture crops classification and the second one, in the area of non-destructive industrial inspection.

Paper Details

Date Published: 2 November 2004
PDF: 9 pages
Proc. SPIE 5558, Applications of Digital Image Processing XXVII, (2 November 2004); doi: 10.1117/12.561069
Show Author Affiliations
Maite Trujillo San-Martin, Brunel Univ. (United Kingdom)
Mustapha Sadki, Brunel Univ. (United Kingdom)


Published in SPIE Proceedings Vol. 5558:
Applications of Digital Image Processing XXVII
Andrew G. Tescher, Editor(s)

© SPIE. Terms of Use
Back to Top