Estimation of Parameters to Model a Fabric in a Way to Identify Defects

  • V. Subhashree
  • S. PadmavathiEmail author
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)


Fabric defect detection is a quality check process which can locate and identify defects caused during the production process in the textile industry. Automated defect identification system uses computer vision and pattern recognition techniques whose performance depends majorly on the quality and quantity of the input dataset. A wide range of parameters is considered for decision process which compromises the accuracy of the system. This paper aims to estimate suitable parameters for the defect-free fabric which can be used by traditional methods to identify the defects in an efficient way. Hough-transform-based method is proposed to identify the parameters and the algorithm is experimented on various fabrics. The proposed method gives promising results when the horizontal and vertical threads are evident in the image.


Defect detection Quality assurance Textile industry Morphological operations Hough transform 


  1. 1.
    Yildiz1 K, Senyürek1 VY, Yildiz Z (2014) A new approach to the determination of warp-weft densities in textile fabrics by using an image processing technique. J Eng Fibers Fabr 2(1)CrossRefGoogle Scholar
  2. 2.
    Priya S, kumar TA, Paul V (2011) A novel approach to fabric defect detection using digital image processing. In; 2011 international conference on signal processing, communication, computing and networking technologies (ICSCCN). IEEEGoogle Scholar
  3. 3.
    Tiwari Vikrant, Sharma Gaurav (2015) Automatic fabric fault detection using morphological operations on bit plane. Int J Comput Sci Netw Secur (IJCSNS) 15(10):30Google Scholar
  4. 4.
    Shi M, Fu R, Guo Y, Bai Sh, Xu B (2011) Fabric defect detection using local contrast deviations. In: Multimedia tools and applications, vol. 52, no. I, pp. 147–157CrossRefGoogle Scholar
  5. 5.
    Lin Chun-Cheng, Yeh Cheng-Yu (2009) Texture defect detection system with image deflection compensation. WSEAS Trans Comput 8(9):1575–1586Google Scholar
  6. 6.
    Mohanaiah P, Sathyanarayana P, GuruKumar L (2013) Image texture feature extraction using GLCM approach. Int J Sci Res Publ 3(5):1Google Scholar
  7. 7.
    Pan, R et al (2010) Automatic inspection of woven fabric density of solid colour fabric density by the Hough transform. Fibres Text East Eur 18(4): 81Google Scholar
  8. 8.
    Yildiz K, et al (2014) A new approach to the determination of warp-weft densities in textile fabrics by using an image processing technique. J Eng Fabr Fibers (JEFF) 9(1)CrossRefGoogle Scholar
  9. 9.
    Padmavathi S, Prem P, Praveenn D (2013) Locating fabric defects using gabor filters. Int Res Eng J Sci Technol (IJSRET) 2(8):472–478Google Scholar
  10. 10.
    Bovik A, Clark M, Geisler W (1990) Multichannel texture analysis using localised spatial filters. IEEE Truns PAMI 12(1):55–72CrossRefGoogle Scholar
  11. 11.
    Michalewicz Z (1996) Genetic Algorithms + Data Structures = Evolution Programs, AI Series, 3rd edn. Springer, New YorkzbMATHGoogle Scholar
  12. 12.
    Tsaia DM, Wua SK, Chen MC (2001) Optimal Gabor filter design for texture segmentation using stochastic optimization. Image Vis Comput 19:299–316CrossRefGoogle Scholar
  13. 13.
    Daugman JG (1985) Uncertainty relations for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. J Opt Soc Am A 2:1160–1169CrossRefGoogle Scholar
  14. 14.
    Sakhare K, Kulkami A, Kumbhakam M (2015) Spectral and spatial domain approach for fabric defect detection and classification. In: 2015 international conference on industrial instrumentation and control (ICIC), 28–30 May 2015Google Scholar
  15. 15.
    Hari CV et al (2009) Mid-point hough transform: a fast line detection method. In: India conference (INDICON), 2009 annual IEEE. IEEEGoogle Scholar
  16. 16.
    Wang Xin, Georganas Nicolas D, Petriu Emil M (2011) Fabric texture analysis using computer vision techniques. IEEE Trans Instrum Meas 60(1):44–56CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringAmrita School of Engineering, Coimbatore, Amrita Vishwa VidyapeethamCoimbatoreIndia

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