21 March 2000 Optimized configuration of systems for texture analysis
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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%.
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Christian Kueblbeck, Christian Kueblbeck, "Optimized configuration of systems for texture analysis", Proc. SPIE 3966, Machine Vision Applications in Industrial Inspection VIII, (21 March 2000); doi: 10.1117/12.380065; https://doi.org/10.1117/12.380065

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