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Proceedings Paper

Optimized configuration of systems for texture analysis
Author(s): Christian Kueblbeck
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Paper Abstract

This paper shows an approach to automatically configured a system for texture analysis. It is examined, how each of the four modules preprocessing, feature extraction, training and classification can be improved. The involved methods for optimization are deterministic selection tools and genetic algorithms. Four different sample sets are used in order to test the proposed methods. It turns out that the greatest decrease in error rate can be reached by optimizing the module feature extraction. Thus the error rate of the classification system can be decreased by approximately 40%.

Paper Details

Date Published: 21 March 2000
PDF: 11 pages
Proc. SPIE 3966, Machine Vision Applications in Industrial Inspection VIII, (21 March 2000); doi: 10.1117/12.380065
Show Author Affiliations
Christian Kueblbeck, Fraunhofer Institute for Integrated Circuits (Germany)


Published in SPIE Proceedings Vol. 3966:
Machine Vision Applications in Industrial Inspection VIII
Kenneth W. Tobin; John C. Stover, Editor(s)

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