Abstract
Yarn hairiness is one of the key parameters influencing fabric quality. In this paper image processing and analysis algorithms developed for an automatic determination of yarn hairiness are presented. The main steps of the proposed algorithms are as follows: image preprocessing, yarn core extraction using graph cut method, yarn segmentation using high pass filtering based method and fibres extraction. The developed image analysis algorithms quantify yarn hairiness by means of the two proposed measures such as hair area index and hair length index, which are compared to the USTER hairiness index—the popular hairiness measure, used nowadays in textile science, laboratories and industry. The detailed description of the proposed approach is given. The developed method is verified experimentally for two distinctly different yarns, produced by the use of different spinning methods, different fibres types and characterized by totally different hairiness. The proposed algorithms are compared with computer methods previously used for yarn properties assessment. Statistical parameters of the hair length index (mean absolute deviation, standard deviation and coefficient of variation) are calculated. Finally, the obtained results are analyzed and discussed. The proposed approach of yarn hairiness measurement is universal and the presented algorithms can be successfully applied in different vision systems for yarn quantitative analysis.
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Acknowledgments
The authors would like to thank Mr Marcin Kuzanski from the Computer Engineering Department for providing the yarn photographs, Professor Tadeusz Jackowski with his research staff from the Department of Spinning Technology and Yarn Structure, Faculty of Textile Engineering and Marketing, TUL for providing yarn testing apparatus and for valuable consultations. We also thank students and researchers from the Faculty of Electrical, Electronic, Computer and Control Engineering, TUL for taking part in comparison tests. Finally, we are grateful to the authors of references [15,22,33,38,47,53] for their agreement to use images shown in Fig. 8. This research was partially supported by Ministry of Science and Higher Education of Poland in a framework of the research project no. N N516 490439 (funds for science in years 2010-2012). Additionally, Anna Fabijańska receives financial support from the Foundation for Polish Science in a framework of START fellowship.
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Fabijańska, A., Jackowska-Strumiłło, L. Image processing and analysis algorithms for yarn hairiness determination. Machine Vision and Applications 23, 527–540 (2012). https://doi.org/10.1007/s00138-012-0411-y
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DOI: https://doi.org/10.1007/s00138-012-0411-y