Generalized and optimized classification framework for textural imagery

Maite Trujillo San-Martin; Mustapha Sadki

doi:10.1117/12.561069

2 November 2004 Generalized and optimized classification framework for textural imagery

Maite Trujillo San-Martin, Mustapha Sadki

Proceedings Volume 5558, Applications of Digital Image Processing XXVII; (2004) https://doi.org/10.1117/12.561069
Event: Optical Science and Technology, the SPIE 49th Annual Meeting, 2004, Denver, Colorado, United States

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.

Citation Download Citation

Maite Trujillo San-Martin and Mustapha Sadki "Generalized and optimized classification framework for textural imagery", Proc. SPIE 5558, Applications of Digital Image Processing XXVII, (2 November 2004); https://doi.org/10.1117/12.561069

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