A Novel Computer Vision Method for Evaluating Deformations of Fibers Cross Section in False Twist Textured Yarns
In recent five decades, textured yarns of polyester fiber produced by false twist method are the most
important and mass-produced manmade fibers. There are
many parameters of cross section which affect the physical and mechanical properties of textured yarns. These parameters
are surface area, perimeter, equivalent diameter, large
diameter, small diameter, convexity, stiffness, eccentricity, and hydraulic diameter. These parameters were evaluated by
digital image processing techniques. To find trends between production criteria and evaluated parameters of cross section, three criteria of production line have been adjusted and different types of yarns were produced. These criteria are
temperature, drafting ratio, and D/Y ratio. Finally the relations between production criteria and cross section parameters were
considered. The results showed that the presented technique can recognize and measure the parameters of fiber cross section in acceptable accuracy. Also, the optimum condition
of adjustments has been estimated from results of image analysis evaluation.
[1] D.P. Thibodeaux, and J.P. Evans, Cotton Fiber Maturity by Image Analysis, Textile Res. J., Vol. 56, No. 2, 1986, pp. 130-139
[2] B. Xu, B. Pourdeyhimi, Evaluating Maturity of Cotton Fibers Using Image Analysis: Definition and Algorithm, Textile Res. J., Vol 64, 1994,
pp. 330-335
[3] T.Schneider, and D. Retting, Chances and Basic Conditions for Determining Cotton Maturity by Image Analysis, In proceedings of the international on cotton testing methods, Bremen, Germany, 1999,pp. 7172
[4] J. Berlin, S. Worley, and H. Ramey, Measuring the Cross-Sectional Area of Cotton Fibers with an Image Analyzer, Textile Res. J., 51, 1981,pp. 109-113
[5] J.J. Hebert, E.K. Boylston, and J.I. Wadsworth, Cross-Sectional Parameters of Cotton Fibers ,Textile Res. J., Vol. 49, No. 9, 1979, pp.
540-542
[6] B. Xu, B. Pourdeyhimi, and J. Sobus, Fiber Cross Sectional Shape
Analysis Using Image Analysis Techniques, Textile Res. J., Vol. 63, No. 12,1993, pp. 717-730
[7] S. Chiu, J. Chen, and J. Lee, Fiber Recognition and Distribution Analysis of PET/Rayon Composite Yarn Cross Sections Using Image
Processing Techniques, Textile Res. J., Vol. 69, No. 6, 1999, pp. 417422
[8] S. Chiu, and J. Liaw, Fiber Recognition of PET/Rayon Composite Yarn
Cross Sections Using Voting Techniques, Textile Res. J., Vol. 75, No. 5, 2005, pp. 442-448
[9] B.K. Behera,
Image Processing in Textiles, The Textile Institute 2004
[10] T. Zhang, N. Sang, G. Wang, and X. Li, An effective method for identifying small objects on a complicated background, Artificial
Intelligence in Engineering, 10 (4), 1996, pp. 343-349
[1] D.P. Thibodeaux, and J.P. Evans, Cotton Fiber Maturity by Image Analysis, Textile Res. J., Vol. 56, No. 2, 1986, pp. 130-139
[2] B. Xu, B. Pourdeyhimi, Evaluating Maturity of Cotton Fibers Using Image Analysis: Definition and Algorithm, Textile Res. J., Vol 64, 1994,
pp. 330-335
[3] T.Schneider, and D. Retting, Chances and Basic Conditions for Determining Cotton Maturity by Image Analysis, In proceedings of the international on cotton testing methods, Bremen, Germany, 1999,pp. 7172
[4] J. Berlin, S. Worley, and H. Ramey, Measuring the Cross-Sectional Area of Cotton Fibers with an Image Analyzer, Textile Res. J., 51, 1981,pp. 109-113
[5] J.J. Hebert, E.K. Boylston, and J.I. Wadsworth, Cross-Sectional Parameters of Cotton Fibers ,Textile Res. J., Vol. 49, No. 9, 1979, pp.
540-542
[6] B. Xu, B. Pourdeyhimi, and J. Sobus, Fiber Cross Sectional Shape
Analysis Using Image Analysis Techniques, Textile Res. J., Vol. 63, No. 12,1993, pp. 717-730
[7] S. Chiu, J. Chen, and J. Lee, Fiber Recognition and Distribution Analysis of PET/Rayon Composite Yarn Cross Sections Using Image
Processing Techniques, Textile Res. J., Vol. 69, No. 6, 1999, pp. 417422
[8] S. Chiu, and J. Liaw, Fiber Recognition of PET/Rayon Composite Yarn
Cross Sections Using Voting Techniques, Textile Res. J., Vol. 75, No. 5, 2005, pp. 442-448
[9] B.K. Behera,
Image Processing in Textiles, The Textile Institute 2004
[10] T. Zhang, N. Sang, G. Wang, and X. Li, An effective method for identifying small objects on a complicated background, Artificial
Intelligence in Engineering, 10 (4), 1996, pp. 343-349
@article{"International Journal of Information, Control and Computer Sciences:54530", author = "Dariush Semnani and Mehdi Ahangareianabhari and Hossein Ghayoor", title = "A Novel Computer Vision Method for Evaluating Deformations of Fibers Cross Section in False Twist Textured Yarns ", abstract = "In recent five decades, textured yarns of polyester fiber produced by false twist method are the most
important and mass-produced manmade fibers. There are
many parameters of cross section which affect the physical and mechanical properties of textured yarns. These parameters
are surface area, perimeter, equivalent diameter, large
diameter, small diameter, convexity, stiffness, eccentricity, and hydraulic diameter. These parameters were evaluated by
digital image processing techniques. To find trends between production criteria and evaluated parameters of cross section, three criteria of production line have been adjusted and different types of yarns were produced. These criteria are
temperature, drafting ratio, and D/Y ratio. Finally the relations between production criteria and cross section parameters were
considered. The results showed that the presented technique can recognize and measure the parameters of fiber cross section in acceptable accuracy. Also, the optimum condition
of adjustments has been estimated from results of image analysis evaluation. ", keywords = "Computer Vision, Cross Section Analysis, Fibers Deformation, Textured Yarn", volume = "3", number = "1", pages = "75-5", }