Skip to main content
SpringerLink
Log in

Fault segmentation in fabric images using Gabor wavelet transform

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

Gabor wavelets have been successfully applied for a variety of machine vision applications such as Texture segmentation, Edge detection, Boundary detection etc. As the Fourier transform is not suitable for detecting local defects, and the Wavelet transforms posses only limited number of orientations, Gabor wavelet transform is chosen and applied to detect the defects in fabrics. Gabor filters scheme that imitates the early human vision process is applied to the sample under inspection. Defects can be automatically segmented from the regular texture by applying the proposed method. Proper thresholding ensures segmentation of the defect from the texture background. The results obtained using this method confirms its efficiency. This can also be applied to detect defects on surfaces and materials that have regular periodic texture.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Wood, E.I.: Applying Fourier and associated transforms to pattern characterisation in textiles. Textile Research Journal 60, 212–220 (1990)

    Article  Google Scholar 

  2. Millan, M.S., Escofet, J.: Fourier domain based angular correlation for quasi periodic pattern recognition applications to Web inspection. Appl. Opt. 35, 6253–6260 (1996)

    Article  Google Scholar 

  3. Escofet, J., Millán, M.S., Ralló, M.: Modelling of woven fabric structures based on Fourier image analysis. Appl. Opt. 40, 6170–6176 (2001)

    Article  Google Scholar 

  4. Mallat, S.: Multi-resolution approximations and wavelet orthonormal bases of L2(R). Transactions of American Mathematical Society 315, 69–87 (1989)

    Article  MATH  MathSciNet  Google Scholar 

  5. Mallat, S.: A theory for multi-resolution signal decomposition: The wavelet representation. IEEE Transactions on Pattern Recognition and Machine Intelligence 11, 674–693 (1989)

    Article  MATH  Google Scholar 

  6. Henke-Reed, M.B.: Cloth Texture Classification Using Wavelet Transform. JIST 37, 610–614 (1993)

    Google Scholar 

  7. Laine, A., Fan, J.: Texture Classification by Wavelet Packet Signatures. IEEE Transactions on Pattern Analysis and Machine Intelligence 15, 1186–1191 (1993)

    Article  Google Scholar 

  8. Chang, T., Kuo, C.C.J.: Texture Analysis and Classification with Tree-Structured Wavelet Transform. IEEE Transactions on Image Processing 2, 429–441 (1994)

    Article  Google Scholar 

  9. Jasper, W.J., Garnier, S.J., Potlapalli, H.: Texture characterization and defect detection using adaptive wavelets. Opt. Eng. 35, 3140–3149 (1996)

    Article  Google Scholar 

  10. Wang, J.W., Chen, C.H., Chien, W.M., Tsai, C.M.: Texture classification using non-separable two-dimensional wavelets. Pattern Recognition Letters 19, 1225–1234 (1998)

    Article  MATH  Google Scholar 

  11. Van De Wouwer, G., Scheunders, P., Van Dyck, D.: Statistical texture characterization from discrete wavelet representations. IEEE Transactions on Image Processing 8, 592–598 (1999)

    Article  Google Scholar 

  12. Wang, L., Liu, J.: Texture classification using multi resolution Markov random field models. Pattern Recognition Letters 20, 171–182 (1999)

    Article  Google Scholar 

  13. Portilla, J., Simoncelli, E.P.: A Parametric texture model based on joint statistics of complex wavelet coefficients. IJCV 40, 49–70 (2000)

    Article  MATH  Google Scholar 

  14. Acharyya, M., Kundu, M.K.: An adaptive approach to unsupervised texture segmentation using M-Band wavelet transform. Signal Processing 81, 1337–1356 (2001)

    Article  MATH  Google Scholar 

  15. Charalampidis, D., Kasparis, T.: Wavelet-based rotational invariant roughness features for texture classification and segmentation. IEEE Transactions on Image Processing 11, 825–837 (2002)

    Article  Google Scholar 

  16. Arivazhagan, S., Ganesan, L.: Texture classification using wavelet transform. International Journal of Pattern Recognition Letters 24, 1513–1521 (2003)

    Article  MATH  Google Scholar 

  17. Arivazhagan, S., Ganesan, L.: Texture segmentation using wavelet transform. International Journal of Pattern Recognition Letters 24, 3197–3203 (2003)

    Article  Google Scholar 

  18. Unser, M., Eden, M.: Multi-resolution feature extraction and selection for texture segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 2, 717–728 (1989)

    Article  Google Scholar 

  19. Marcelja, S.: Mathematical description of the responses of simple cortical cells. Journal of optical society of America 70, 1297–1300 (1980)

    Article  MathSciNet  Google Scholar 

  20. Valors, R.De., Valors, K.De.: Spatial Vision. New York, Oxford (1988)

  21. Zhang, D.S., Wong, A., Indrawan, M., Lu, G.: Content based image retrieval using Gabor texture features. In Proc. of 1st IEEE Pacific Rim conference on Multimedia (PCM'00), pp 392–395 (2000)

  22. Daugman, J.G.: Two-dimensional spectral analysis of cortical receptive field profiles. Vision Res. 20, 847–856 (1980)

    Article  PubMed  Google Scholar 

  23. Daugman, J.G.: Uncertainty relation for resolution in space, spatial frequency and orientation optimized by two-dimensional visual cortical filters. Journal of Optical Society, America 2, 1160–1169 (1985)

    Article  Google Scholar 

  24. Daugman, J.G.: Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression. IEEE Transactions on Acoustics, Speech, Signal Processing 36, 1169–1179 (1988)

    Article  MATH  Google Scholar 

  25. Young, R.A.: The Gaussian derivative model for spatial vision: I. Retinal mechanisms. Spatial visions 2, 273–293 (1987)

    Article  Google Scholar 

  26. Tuner, M.R.: Texture discrimination by Gabor functions. Biol. Cyber. 55, 71–82 (1986)

    Google Scholar 

  27. Fogel, I., Sagi, D.: Gabor filters as texture discriminator. Biol. Cybern. 61, 103–113 (1986)

    Google Scholar 

  28. Nestares, O., Navarro, R., Portilla, J.: Efficient spatial domain implementation of a multi-scale image representation based on Gabor functions. Electronic Imaging 7, 166–173 (1998)

    Article  Google Scholar 

  29. Coggins, J.M., Jain, A.K.: A spatial filtering approach to texture analysis. Pattern Recognition Letters 3, 195–203 (1985)

    Article  Google Scholar 

  30. Porat, M., Zeevi, Y.Y.: Localized texture processing in vision Analysis and Synthesis in Gaborian space. IEEE Transactions on Biomedical Engg. 36, 115–129 (1989)

    Article  Google Scholar 

  31. Bovik, A.C., Clark, M., Geisler, W.S.: Multi-channel Texture Analysis Using Localized Spatial Filters. IEEE Transactions on Pattern Analysis and Machine Intelligence 12, 55–73 (1990)

    Article  Google Scholar 

  32. Bovik, A.C., Gopal, N., Emmoth, T., Restrepo, A.: Localized measurement of emergent image frequencies by Gabor wavelets. IEEE Transactions on Information Theory 38, 691–712 (1992)

    Article  Google Scholar 

  33. Du Buf, J.M.H.: Gabor phase in texture discrimination. Signal Processing 21, 221–240 (1990)

    Article  Google Scholar 

  34. Jain, A.K., Farrokhnia, F.: Unsupervised texture segmentation using gabor filters. Pattern Recognition 24, 1167–1186 (1991)

    Article  Google Scholar 

  35. Bigün, J., Du Buf, J.M.H.: N-Folded symmetries by complex moments in gabor space and their application to unsupervised texture segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 16, 80–87 (1994)

    Article  Google Scholar 

  36. Dunn, D.F., Higgins, W.E., Wakeley, J.: Texture segmentation using 2-D gabor elementary functions. IEEE Transactions on Pattern Analysis and Machine Intelligence 16, 130–149 (1994)

    Article  Google Scholar 

  37. Dunn, D.F., Higgins, W.E.: Optimal gabor filters for texture segmentation. IEEE Transactions on Image Processing 4, 947–964 (1995)

    Article  Google Scholar 

  38. Teuner, A., Pichler, O., Hosticka, B.J.: Unsupervised texture segmentation of images using tuned matched gabor filters. IEEE Transactions on Image Processing 4, 863–870 (1995)

    Article  Google Scholar 

  39. Haley, G.M., Manjunath, B.S.: Rotation invariant texture classification using modified Gabor filters. In Proc. IEEE Int. Conf. Image Processing, Washington, DC (1995)

  40. Haley, G.M., Manjunath, B.S.: Rotation-invariant texture classification using a complete space-frequency model. IEEE Transactions on Image Processing 4, 255–269 (1999)

    Article  Google Scholar 

  41. Manjunath, B.S., Ma, W.Y.: Texture features for browsing and retrieval of image data. IEEE Transactions on Pattern Analysis and Machine Intelligence 18, 837–842 (1996)

    Article  Google Scholar 

  42. Pichler, O., Teuner, A., Hosticka, B.J.: A Comparison of texture feature-extraction using adaptive gabor filtering, pyramidal and tree-structured wavelet transforms. Pattern Recognition 29, 733–742 (1996)

    Article  Google Scholar 

  43. Idrissa, M., Acheroy, M.: Texture classification using Gabor filters. Pattern Recognition Letters 23, 1095–1102 (2002)

    Article  MATH  Google Scholar 

  44. Manthalkar, R., Biswas, P.K., Chatterji, B.N.: Rotation invariant texture classification using even symmetric Gabor filters. Pattern Recognition Letters 24, 2061–2068 (2003)

    Article  Google Scholar 

  45. Jasper, W.J., Potlapalli, H.: Image analysis of mis-picks in woven fabric. Textile Research Journal 65, 683–692 (1995)

    Article  Google Scholar 

  46. Hosseini, R.S.A.: Fourier transform analysis of plain weave fabric appearance. Textile Research Journal 65, 676–683 (1995)

    Article  Google Scholar 

  47. Ciamberlini, C., Francini, F., Longobardi, G., Sansoni, P., Tiribilli, B.: Defect detection in textured materials by optical filtering with structured detectors and self adaptable masks. Optical Engineering 35, 838–844 (1996)

    Article  Google Scholar 

  48. Escofet, J., Navarro, R., Millan, M.S., Paldellorens, J.: Detection of local defects in textile webs using Gabor filters. Optical Engineering 37, 2297–2307 (1998)

    Article  Google Scholar 

  49. Kang, T.J., Kim, C.H., Oh, K.W.: Automatic recognition of fabric weave patterns by digital image analysis. Textile Research Journal 69, 77–83 (1999)

    Article  Google Scholar 

  50. Kang, T.J., Choi, S.H., Kim, S.M., Oh, K.W.: Automatic structure analysis and objective evaluation of woven fabric using image analysis. Textile Research Journal 71, 261–270 (2001)

    Google Scholar 

  51. Hu, M.C., Tsai, I.S.: Fabric inspection based on best wavelet packet -bases. Textile Research Journal 70, 662–670 (2000)

    Article  Google Scholar 

  52. Ralló, M., Millán, M.S., Escofet, J.: Wavelet based techniques for textile inspection. http://www.imub.ub.es/wavelets/Rallo.pdf (2001)

  53. Kumar, A., Pang, G.K.H.: Defect detection in textured materials using optimized filters. IEEE Transactions on Systems Man and Cybernetics 32, 553–570 (2002)

    Article  Google Scholar 

  54. Abdulghafour, M., Goddard, J.S., Abidi, M.A.: Non deterministic approaches in data fusion—A review. In Proceedings of SPIE conference on Sensor fusion, Boston, MA 1393, 596–610 (1990)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of ECE, Mepco Schlenk Engineering College, 626005, Sivakasi, India

    S. Arivazhagan & S. Bama

  2. Department of CSE, A.C. College of Engg.& Technology, 630004, Karaikudi, India

    L. Ganesan

Authors
  1. S. Arivazhagan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. L. Ganesan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S. Bama
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. Arivazhagan.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Arivazhagan, S., Ganesan, L. & Bama, S. Fault segmentation in fabric images using Gabor wavelet transform. Machine Vision and Applications 16, 356–363 (2006). https://doi.org/10.1007/s00138-005-0007-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-005-0007-x

Keywords

Search

Navigation