{"references": ["D.P. Thibodeaux, and J.P. Evans, Cotton Fiber Maturity by Image Analysis, Textile Res. J., Vol. 56, No. 2, 1986, pp. 130-139", "B. Xu, B. Pourdeyhimi, Evaluating Maturity of Cotton Fibers Using Image Analysis: Definition and Algorithm, Textile Res. J., Vol 64, 1994,\npp. 330-335", "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", "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", "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.\n540-542", "B. Xu, B. Pourdeyhimi, and J. Sobus, Fiber Cross Sectional Shape\nAnalysis Using Image Analysis Techniques, Textile Res. J., Vol. 63, No. 12,1993, pp. 717-730", "S. Chiu, J. Chen, and J. Lee, Fiber Recognition and Distribution Analysis of PET/Rayon Composite Yarn Cross Sections Using Image \nProcessing Techniques, Textile Res. J., Vol. 69, No. 6, 1999, pp. 417422", "S. Chiu, and J. Liaw, Fiber Recognition of PET/Rayon Composite Yarn\nCross Sections Using Voting Techniques, Textile Res. J., Vol. 75, No. 5, 2005, pp. 442-448", "B.K. Behera,\nImage Processing in Textiles, The Textile Institute 2004\n[10] T. Zhang, N. Sang, G. Wang, and X. Li, An effective method for identifying small objects on a complicated background, Artificial\nIntelligence in Engineering, 10 (4), 1996, pp. 343-349"]}

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.