Fast qualitative analysis of textile fiber in near infrared spectroscopy based on support vector machine

Donghui Wang; Shangzhong Jin; Bin Gan; Hongnian Feng

doi:10.1117/12.674348

9 June 2006 Fast qualitative analysis of textile fiber in near infrared spectroscopy based on support vector machine

Donghui Wang, Shangzhong Jin, Bin Gan, Hongnian Feng

Proceedings Volume 6149, 2nd International Symposium on Advanced Optical Manufacturing and Testing Technologies: Advanced Optical Manufacturing Technologies; 61493C (2006) https://doi.org/10.1117/12.674348
Event: 2nd International Symposium on Advanced Optical Manufacturing and Testing Technologies, 2005, Xian, China

Abstract

The fast qualitative analysis of textile fiber is a crucial step in textile manufacture, export and inspection. This paper presents a near infrared spectroscopy classification method based on SVM for fast qualitative analysis of textile fiber. SVM is a new automatic classification tool and it has successfully been applied to standard classification tasks, such as text classification, pattern identification, bioinformatics and medical diagnosis. In this paper, SVM is extended into near infrared fast qualitative analysis of textile fiber for the first time. In this paper, eight kinds classification algorithms which are composed of two classifiers(C-SVC and ν-SVC) and four kernel functions (linear, polynomial, RBF and sigmoid) are used to do classification experiments and comparison analysis for ten kinds familiar textiles fiber. Experiment results show that it is feasible to apply SVM in fast qualitative analysis of textile fiber, and the optimal classifier algorithm and the corresponding experimental results are reported.

Citation Download Citation

Donghui Wang, Shangzhong Jin, Bin Gan, and Hongnian Feng "Fast qualitative analysis of textile fiber in near infrared spectroscopy based on support vector machine", Proc. SPIE 6149, 2nd International Symposium on Advanced Optical Manufacturing and Testing Technologies: Advanced Optical Manufacturing Technologies, 61493C (9 June 2006); https://doi.org/10.1117/12.674348

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