Machine Vision and Applications

, Volume 23, Issue 3, pp 527–540 | Cite as

Image processing and analysis algorithms for yarn hairiness determination

Open Access
Original Paper


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.


Digital image processing Vision system Image quantitative analysis Yarn hairiness measurement 



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.

Open Access

This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.


  1. 1.
    Altaş S., Kadoğlu H.: Determining fibre properties and linear density effect on cotton yarn hairiness in ring spinning. Fibres Text. Eastern Europe 14(3), 48–51 (2006)Google Scholar
  2. 2.
    Barella, A.: New concepts of yarn hairiness. J. Text. Inst. (Proc.) 47(2), P120–P127Google Scholar
  3. 3.
    Barella A., Martin V., Vigo J.P., Manich A.M.: A new hairiness Meter for yarns. J. Text. Inst. 71(6), 277–283 (1980)CrossRefGoogle Scholar
  4. 4.
    Barella A., Manich A.M.: Yarn hairiness updates. Text. Prog. 26(4), 1–29 (1997)CrossRefGoogle Scholar
  5. 5.
    Barella, A.: Hairiness testing of yarns. In: Kothari, V.K. (ed.) Progress in Textiles: Science & Technology, vol. 1. Testing and Quality Management (1998)Google Scholar
  6. 6.
    Barella A., Manich A.M.: Yarn hairiness: a further update. Text. Prog. 31(4), 1–44 (2002)CrossRefGoogle Scholar
  7. 7.
    Basal G., Oxenham W.: Effects of some process parameters on the structure and properties of vortex spun yarn. Text. Res. J. 76(6), 492–499 (2006)CrossRefGoogle Scholar
  8. 8.
    Benjamin, J.R., Cornell, C.A.: Probability, Statistics, and Decision for Civil Engineers. McGraw-Hill Inc., USA (1970), Polish edition, WNT, Warsaw (1977)Google Scholar
  9. 9.
    Boykov, Y.Y., Jolly, M.-P.: Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images. In: Proc. Int. Conf. on Computer Vision, vol. 1, pp. 105–112, Vancouver, Canada (2001)Google Scholar
  10. 10.
    Boykov, Y.Y., Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Tran. PAMI, 26, 9, 1124–1137 (2004). Accessed 3 May 2011
  11. 11.
    Canny J.: A Computational approach to edge detection. IEEE Trans. PAMI 8(6), 679–698 (1986)CrossRefGoogle Scholar
  12. 12.
    Carvalho, V., Cardoso, P., Vasconcelos, R., Oliveira, F., Belsley, M.: Optical yarn hairiness measurement system. In: Proc. IEEE Int. Conf. on Industrial Informatics, vol. 5, pp. 359–364, Vienna, Austria (2007)Google Scholar
  13. 13.
    Carvalho V., Belsley M., Vasconcelos R., Soares F.O.: Yarn Diameter and linear mass correlation. J. Non-Destruct. Eval. 28(2), 49–54 (2009)CrossRefGoogle Scholar
  14. 14.
    Carvalho, V., Soares, F.O., Vasconcelos R.: Artificial intelligence and image processing based techniques: a tool for yarns parameterization and fabrics prediction. In: Proc. 14th IEEE Int. Conf. on Emerging Technologies & Factory Automation, pp. 1531–1534. Palma de Mallorca, Spain (2009)Google Scholar
  15. 15.
    Chimeh M.Y., Tehran M.A., Latifi M., Mojtahedi M.R.M.: Characterizing bulkiness and hairiness of air-jet textured yarn using imaging techniques. J. Text. Inst. 96(4), 251–255 (2005)CrossRefGoogle Scholar
  16. 16.
    Cybulska M.: Analysis of warp destruction in the process of weaving using the system for assessment of the yarn structure. Fibres Text. Eastern Europe 5(4), 68–72 (1997)Google Scholar
  17. 17.
    Cybulska M.: Assessing yarn structure with image analysis methods. Text. Res. J. 69, 369–373 (1999)CrossRefGoogle Scholar
  18. 18.
    Drobina R., Machnio M.S.: Application of the image analysis technique for estimating the dimensions of spliced connections of yarn-ends. Fibres Text. Eastern Europe 14(3), 63–69 (2006)Google Scholar
  19. 19.
    Drobina R., Machnio M.S.: Application of the image analysis technique for textile identification. AUTEX Res. J. 6(1), 40–48 (2006)Google Scholar
  20. 20.
    Fabijańska, A., Kuzański, M., Sankowski, D., Jackowska-Strumiłło, L.: Application of Image Processing and Analysis in Selected Industrial Computer Vision Systems. In: Proc. IEEE Int. Conf. Perspective Technologies and Methods in Mems Design, Lviv-Polyana, Ukraine, pp. 27–31 (2008)Google Scholar
  21. 21.
    Gonzalez R., Woods E.: Image Processing. Prentice Hall, New Jersey (2007)Google Scholar
  22. 22.
    Guha A., Amarnath C., Pateria S., Mittal R.: Measurement of yarn hairiness by digital image processing. J. Text. Inst. 99(6), 1754–2340 (2009)Google Scholar
  23. 23.
    Jackowski T.: The hairiness of two-component yarns. Przeglad Włokienniczy (in Polish) 15(6), 271–273 (1961)Google Scholar
  24. 24.
    Jackowski T., Chylewska B., Cyniak D.: The hairiness of yarns cotton and cotton type fibres. Fibres Text. Eastern Europe 2, 22–23 (1994)Google Scholar
  25. 25.
    Jackowski, T., Chylewska, B.: Spinning, yarn technology and structure. Technical University of Lodz, Lodz, p. 452 (1999, in Polish)Google Scholar
  26. 26.
    Jackowski T., Cyniak D., Czekalski J.: Influence of selected parameters of the spinning process on the state of mixing of fibres of a cotton/polyester-fibre blend yarn. Fibres Text. Eastern Europe 14(4), 36–40 (2006)Google Scholar
  27. 27.
    Jackson M., Acar M., Siong L.Y., Whitby D.: A Vision-based yarn scanning system. Mechatronics 5(2/3), 133–146 (1995)CrossRefGoogle Scholar
  28. 28.
    Jedryka T.: Method for the determination of hairiness of yarn. Text. Res. J. 33, 663–665 (1963)Google Scholar
  29. 29.
    Kim H.J., Kim J.S., Lim J.H., Huh Y.: Detection of wrapping defects by a machine vision and its application to evaluate the wrapping quality of the ring core spun yarn. Text. Res. J. 79(17), 1616–1624 (2009)CrossRefGoogle Scholar
  30. 30.
    Keisokki Kogyo Co., Ltd.
  31. 31.
    Kuzański, M., Jackowska-Strumiłło, L.: Yarn hairiness determination by the use of image processing and analysis versus classical methods. In: Proc. IEEE Int. Conf. The Experience of Designing and Application of CAD Systems in Microelectronics, Lviv, Ukraine, pp. 405–407 (2005)Google Scholar
  32. 32.
    Kuzański, M.: Measurement methods for yarn hairiness analysis—the idea and construction of research standing. In: Proc. IEEE Int. Conf. Perspective Technologies and Methods in Mems Design, Lviv-Polyana, Ukraine, pp. 87–90 (2006)Google Scholar
  33. 33.
    Kuzański, M., Jackowska-Strumiłło, L.: Yarn hairiness determination—the algorithms of computer measurement methods. In: Proc. IEEE Int. Conf. Perspective Technologies and Methods in Mems Design, Lviv-Polyana, Ukraine, pp. 154–157 (2007)Google Scholar
  34. 34.
    Lappage J., Onions W.J.: An instrument for the study of yarn hairiness. J. Text. Inst. (Transactions) 55(8), T381–T395 (1964)CrossRefGoogle Scholar
  35. 35.
    Masajtis J.: Thread image processing in the estimation of repetition of yarn structure. Fibres Text. Eastern Europe 5(3), 35–37 (1997)Google Scholar
  36. 36.
    Onions W.J., Yates M.: The photoelectric measurement of the irregularity and the hairiness of worsted yarn. J. Text. Institute (Transactions) 45(12), T873–T885 (1954)Google Scholar
  37. 37.
    Otsu N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man. Cyber. 9(1), 62–66 (1979)MathSciNetCrossRefGoogle Scholar
  38. 38.
    Ozkaya, Y.A., Acar, M., Jackson, M.R.: Digital image processing and illumination techniques for yarn characterization. J. Electron. Imaging 14(2) (2005). doi: 10.1117/1.1902743
  39. 39.
    Ozkaya Y.A., Acar M., Jackson M.R.: Hair density distribution profile to evaluate yarn hairiness and its application to fabric simulations. J. Text. Inst. 98(6), 483–490 (2007)CrossRefGoogle Scholar
  40. 40.
    Ozkaya Y.A., Acar M., Jackson M.R.: Simulation of photosensor-based hairiness measurement using digital image analysis. J. Text. Inst. 99(2), 93–100 (2008)CrossRefGoogle Scholar
  41. 41.
    Ozkaya Y.A., Acar M., Jackson M.R.: Yarn twist measurement using digital imaging. J. Text. Inst. 101(2), 91–100 (2010)CrossRefGoogle Scholar
  42. 42.
    Pillay K.P.R.: A study of yarn hairiness in cotton yarns. Part I: effect of fibre and yarn factors. Text. Res. J. 34(8), 663–674 (1964)CrossRefGoogle Scholar
  43. 43.
    Princeton Instruments. Accessed 14 March 2010
  44. 44.
    Rodrigues F.C., Silva M.S., Morgado C.: The configuration of a textile yarn in the frequency space: a method of measurement of hairiness. J. Text. Inst 74(4), 161–167 (1983)CrossRefGoogle Scholar
  45. 45.
    Sahoo P.K., Soltani S., Wong K.C., Chen Y.C.: A survey of thresholding techniques. Comput. Vis. Graph. Image Process. 41, 233–260 (1988)CrossRefGoogle Scholar
  46. 46.
    Saville B.P.: Physical Testing of Textiles. Woodhead, Cambridge (1999)CrossRefGoogle Scholar
  47. 47.
    Sparavigna A., Broglia E., Lugli S.: Beyond capacitive systems with optical measurements for yarn evenness evaluation. Mechatronics 14, 1183–1196 (2004)CrossRefGoogle Scholar
  48. 48.
    Szydĺowski, H.: Theory of Measurement. PWN, Warsaw (1981, in Polish)Google Scholar
  49. 49.
    Tang N.K.H., Pickering J.F., Freeman J.M.: An investigation into the control of brushed yarn properties: the application of machine vision and knowledge-based systems. Part II: the machine vision system. J. Text. Inst. 84(2), 166–175 (1993)CrossRefGoogle Scholar
  50. 50.
  51. 51.
  52. 52.
    Wang, X.-H., Wang, J.-Y., Zhang, J.-L., Liang, H.-W., Kou, P.-M.: Study on the detection of yarn hairiness morphology based on image processing technique. In: Proc. Int. Conf. Machine Learning and Cybernetics, Guilin, China, vol. 5, pp. 2332–2336 (2010)Google Scholar
  53. 53.
    Yarn Image from Accessed 8 May 2011
  54. 54.
    Zhang, X., Gao, W., Liu, J.: Automatic recognition of yarn count in fabric based on digital image processing. In: Proc. Congress on Image and Signal Processing, vol. 3, pp. 100–103, Sanya, China (2008)Google Scholar
  55. 55.
    Zurek W., Krucińska I., Adrian H.: Distribution of component fibres on the surface of blend yarns. Text. Res. J. 52, 473–478 (1982)CrossRefGoogle Scholar

Copyright information

© The Author(s) 2012

Authors and Affiliations

  1. 1.Computer Engineering DepartmentTechnical University of Lodz (TUL)LodzPoland

Personalised recommendations