Evaluation of Defect Detection in Textile Images Using Gabor Wavelet Based Independent Component Analysis and Vector Quantized Principal Component Analysis

  • S. Anitha
  • V. Radha
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 222)


Textile defect detection plays an important role in the manufacturing industry to maintain the quality of the end product. Wavelet transform is more suitable for quality inspection due to its multi-resolution representation. The Gabor Wavelet Network provides an effective way to analyze the input images and to extract the texture features. The paper addresses the functionality of Gabor wavelet network with independent component analysis and vector quantized principal component analysis. The two methods are used to extract the features from the template image. Then the difference between the template image and the input image features are compared, and threshold value is calculated using Otsu method to obtain the binary image. The performances of the methods are evaluated to verify the efficiency in identifying the defect in the pattern fabric image.


Defect detection Gabor wavelet Fabric Independent component analysis Principal component analysis 


  1. 1.
    Manjunath B, Chellappa R (1993) A unified approach to boundary perception: edges, textures, and illusory contours. IEEE Trans Neural Netw 4(1):96–107CrossRefGoogle Scholar
  2. 2.
    Kr¨uger V (2001) Gabor wavelet networks for object representation, Tech. Rep CS-TR-4245, University of Maryland, CFAR, MayGoogle Scholar
  3. 3.
    Mak KL, Peng P, (2005) “Defect detection in textile fabrics using gabor networks”,18th international conference on computer applications in industry and engineering, USA, Nov 9–11, pp 226–231Google Scholar
  4. 4.
    Serdaroglu A, Ertuzun A, Ercil A (2006) Defect detection in textile fabric im-ages using wavelet transforms and independent component analysis. Pattern Recog Image Anal 16(1):61–64CrossRefGoogle Scholar
  5. 5.
    Turk M, Pentland A (1991) Eigenfaces for recognition. J Cognitive Neurosci 3(1):71–86CrossRefGoogle Scholar
  6. 6.
    Shlens (2009) “A tutorial on principal component analysis.” {$\sim$shlens/
  7. 7.
    Eleyan A, Demirel H (2007) “PCA and LDA based neural networks for human face recognition recognition”. In: Delac K, Grgic M (eds) Face recognition, June Google Scholar
  8. 8.
    Wang J, Campbell RA, Harwood RJ (1995) “Automated inspection of car-pets”. In: Proceedings of SPIE, vol 2345. pp 180–191Google Scholar
  9. 9.
    Krueger, Feris R (2001) “Wavelet subspace method for real- time face tracking”, Pattern Recognition, 23rd DAGM SymposiumGoogle Scholar
  10. 10.
    Zhang Q, Benveniste A (1992) Wavelet networks. IEEE Trans Neural Netw 3(6):889–898CrossRefGoogle Scholar
  11. 11.
    Daubechies I (1990) The wavelet transform, time-frequency localization and signal analysis. IEEE Trans. Inf Theory 36:961–1005MathSciNetMATHCrossRefGoogle Scholar
  12. 12.
    Mak KL, Peng p, Lau HYK (2005) ”Optimal morphological filter design for fabric defect detection”, IEEE international conference on industrial technology, China,14–17 Dec, pp 799–804Google Scholar
  13. 13.
    Krueger V, Sommer G (2000) Gabor wavelet networks for object representation. DAGM Symposium, Germany, Sept, pp 13–15Google Scholar
  14. 14.
    Hyvärinen A, Oja E (2000) Independent component analysis: algorithms and applications. Neural Network. 13(4/5):411–430CrossRefGoogle Scholar
  15. 15.
    Bell A, Sejnowski T (1995) An information-maximization approach to blind separation and blind deconvolution. Neural Comput 7(6):1129–1159CrossRefGoogle Scholar
  16. 16.
    Cardoso J, Cnrs P (1998) Blind signal separation: statistical principles. Proc IEEE 86(10):2009–2025CrossRefGoogle Scholar
  17. 17.
    Sezer O, Ertüzün A, Erçil A (2004) Independent component analysis for texture defect detection. Pattern Recognit Image Anal 14(2):303–307Google Scholar
  18. 18.
    Tsai D, Lai S (2008) Defect detection in periodically patterned surfaces using independent component analysis. Pattern Recogn 41(9):2812–2832MATHCrossRefGoogle Scholar
  19. 19.
    Rogerio S, F Roberto M (2000) “Tracking facial features using Gabor wavelet networks”, Computer graphics and image proceeding. In: Symposium on Brazalian pro-ceedings XII, pp 22–27Google Scholar
  20. 20.
    Anitha S, Radha V (2012) “Enhanced switching median filter for denoising in 2d patterned textile images”. In: Proceedings of ICMOC, vol 38. Pp 3362–3372Google Scholar
  21. 21.
    Kruger V, Sommer G (2002) Gabor wavelet networks for efficient heard pose estimation. Image Vis Comput 20:665–672CrossRefGoogle Scholar

Copyright information

© Springer India 2013

Authors and Affiliations

  1. 1.Department of Computer ScienceAvinashilingam UniversityCoimbatoreIndia

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