Skip to main content
Log in

Detection of defects in fabrics using topothesy fractal dimension features

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

During the manufacturing of textiles, several types of defects occur in the fabrics. This paper explores the characterization of the fabric textures using the conventional approaches such as Gabor filter, Gabor wavelet and Gauss Markov random field (MRF) and the well-known method for surface roughness measurement in the mechanical engineering called topothesy. The topothesy and fractal dimension known as fractal parameters represent not only the roughness but also the affine self-similarity in fabric textures. The fabric texture features are tested on the database of four types of defective fabric samples, viz., torn fabric, oil stain, miss pick and interlacing of two webs, collected from the cloth mills of Berhampur. A comparison of the results of defect detection in fabrics indicates that the topothesy fractal dimension features outperform those of Gabor filter, Gabor wavelets and Gauss MRF.

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

Access this article

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Arivazhagan, S., Ganesh, L., Bama, S.: Fault segmentation in fabric images using Gabor-wavelet transform. Mach. Vis. Appl. 16(6), 356–363 (2006)

    Article  Google Scholar 

  2. Atalay, A.: Automated Defect Inspection of Textile Fabric Using Machine Vision Techniques, M S Thesis, Bogazici University, Istanbul, Turkey, (1995)

  3. Beirao, C. L., Mar, Figueiredo.: Defect detection in textile images using Gabor filters. In: Proceedings of ICIAR’ 2004, Lecture Notes in Computer Science, vol. 3212, pp. 841–848. Springer (2004)

  4. Bodnarova, A., Bennamoun, M., Latham, S.J.: Optimal Gabor filters for textile flaw detection. Pattern Recognit. 35, 2973–2991 (2002)

    Article  MATH  Google Scholar 

  5. Bradshaw, M.: The application of machine vision to the automated inspection of knitted fabrics. Mechatronics 5(2/3), 233–243 (1995)

    Article  Google Scholar 

  6. Bu, H., Huang, X.: A novel multiple fractal features extraction framework and its application to the detection of fabric defects. J. Text. Inst. 99(5), 489–497 (2008)

    Google Scholar 

  7. Bu, H., Wang, J., Huang, X.: Fabric defects detection based on multiple fractal features and support vector data description. Eng. Appl. Artif. Intell. 22, 224–235 (2009)

    Article  Google Scholar 

  8. Chaudhuri, B.B., Sarkar, N.: Texture segmentation using fractal dimension. IEEE Trans. Pattern Anal. Mach. Intell. 17(1), 72–77 (1995)

    Article  Google Scholar 

  9. Cho, C.S., Chung, B.M., Park, M.J.: Development of real-time vision-based fabric inspection system. IEEE Trans. Ind. Electron. 52(4), 1073–1079 (2005)

    Article  Google Scholar 

  10. Cohen, F.S., Fan, Z., Attali, S.: Automated inspection of textile fabric using textural models. IEEE Trans. Pattern Anal. Mach. Intell. 13(8), 803–808 (1991)

    Article  Google Scholar 

  11. Conci, A., Proenca, C.B.: A fractal image analysis system for fabric inspection system based on box counting method. Comput. Netw. ISDN Syst. 30, 1887–1995 (1998)

    Article  Google Scholar 

  12. David, Zhang, Wai-Kin, Kong, Jane, You, Michael, Wong: Online palmprint identification. IEEE Trans. Pattern Anal. Mach. Intell. 25(9), 1041–1050 (2003)

    Article  Google Scholar 

  13. Dorrity, J. L., Vachtsevanos, G.: In-process fabric defect detection and identification. In: Proceedings of Mechatronics, pp. 745–750 (1998)

  14. Dorrity, J. L., Vachtsevanos, G.: On-line defect detection for weaving systems. In: Proceedings of IEEE Annual Technology Conference on Textile, Fiber Film Industry, pp. 1–6 (1996)

  15. Escofet, J., Navarro, R., Millan, M.S., Pladellorens, J.: Detection of local defects in textiles webs using Gabor filters. Opt. Eng. 37(8), 2297–2307 (1998)

    Article  Google Scholar 

  16. Fuziwara, H., Zhang, Z., Hashimoto, K.: Towards automated inspection of textile surfaces: removing the textural information by using wavelet shrinkage. In: Proceedings of International Conference on Robotics Automation, pp. 3529–3534, Seoul, Korea, 21–26 May (2001)

  17. Giorgilli, A., Casati, D., Sironi, L., Galgani, L.: An efficient procedure to compute fractal dimensions by box counting. Phys. Lett. A 115(5), 202–206 (1986)

    Article  MathSciNet  Google Scholar 

  18. Hanmandlu, M., Agarwal, S., Das, A.: A comparative study of different texture segmentation techniques. In: PReMI, Kolkata. Lecture Notes in Computer Science, vol. 3776, pp. 477–480 (2005)

  19. http://www.cvg.rdg.ac.uk/papers/PDF/wei_SlahUnd_bartels_SlahUnd_gaussian_SlahUnd_gabor_SlahUnd_features_SlahUnd_PRRS_SlahUnd_06.pdf

  20. Huart, J., Postaire, J.G.: Integration of computer vision onto weavers for quality control in the textile industry. Proc. SPIE Mach. Vis. Appl. Ind. Insp. II 2183, 155–163 (1994)

    Google Scholar 

  21. Kim, S., Lee, M.H., Woo, K.B.: Wavelet analysis to defect detection in weaving processes. Proc. IEEE Symp. Ind. Electron. 3, 1406–1409 (1999)

    Google Scholar 

  22. Krueger, v.: Gabor Wavelet Networks for Object Representation, Ph.D thesis, Christian Albrecht university, Germany, (2001)

  23. Krueger, V., Somer, G.: Gabor wavelet networks for object representation, DAGM Symposium, Germany, pp. 13–15 (2000)

  24. Krueger, V., Somer, G.: Gabor wavelet networks for efficient head pose estimation. Image Vis. comput. 20, 665–672 (2002)

    Article  Google Scholar 

  25. Kumar, A., Pang, G.: Fabric defect segmentation using multichannel blob detectors. Opt. Eng. 39(12), 3176–3190 (2000)

    Article  Google Scholar 

  26. Kumar, A., K.H. Pang, Grantham: Defect detection in textured materials using optimized filters. IEEE Trans. Syst. Man Cybern. B 32(5), 571–582 (2002)

    Google Scholar 

  27. Liu, X., Wen, Z., Su, Z., Ka-Fai, Choi: Slub extraction in Woven fabric Images using Gabor filters. Text. Res. J. 78, 320–325 (2008)

    Article  Google Scholar 

  28. Mahajan, P.M., Kolhe, S.R., Patil, P.M.: A review of automated fabric defect detection techniques. Adv. Comput. Res. 1(2), 18–29 (2009)

    Google Scholar 

  29. Ngan, H.Y.T., Pang, G.K.H., Yung, N.H.C.: Automated fabric defect detection: a review. Image Vis. Comput. 29, 442–458 (2011)

    Article  Google Scholar 

  30. Norton-Wayne, L., Bradshaw, M., Sandby, C.: Machine vision inspection of web textile fabric. In: Proceedings of the British Machine Vision Conference, pp. 217–226, Leeds, UK, (1992)

  31. Ozdemir, S., Ercil, A.: Markov random fields and Karhunen–Loeve transform for defect inspection of textile products. In: Proceedings of the IEEE Conference on Emerging Technologies and Factory, vol. 2, pp. 697–703. Istambul, Automation, 18–21 November (1996)

  32. Petrou, M., Sevilla, P.G.: Image Processing Dealing with Texture, 1st edn. Wiley, New York (2006)

    Book  Google Scholar 

  33. Rosler, R.N.U.: Defect detection in fabrics by image processing. Melliand Texilber. 73, 635–636 (1992)

    Google Scholar 

  34. Ruilin, Z., Yan, H., Weijie, G., Chenyan, Z.: Multi scale Markov random field based fabric image segmentation associate with edge information. IEEE Proc. Int. Symp. Comput. Intell. Design (ISCID) 12–14, 566–569 (2009)

  35. Russ, J.C.: Fractal Surfaces. Plenum, NewYork (1994)

    Book  Google Scholar 

  36. Sari-Saraf, H., Goddard, J.S.: Vision system for on-loom fabric inspection. IEEE Trans. Ind. Appl. 35, 1252–1259 (1999)

    Article  Google Scholar 

  37. Serdaroglu, A., Ertuzun, A., Ercil, A.: Defect detection in textile fabric images using wavelet transforms and independent component analysis. Pattern Recognit. Image Anal. 16(1), 61–64 (2006)

    Article  Google Scholar 

  38. Shu, Y., Tan, Z.: Fabric defects automatic detection using Gabor filter. In: Proceedings of IEEE 5th World Congress on Intelligent Control and Automation, 4, pp. 3378–3380. Hangzhou. China, 15–19 June (2004)

  39. Siew, L.H., Hodgson, R.M., Wood”, E.J.: Texture measures for carpet wear assessment. IEEE Trans. Pattern Anal. Mach. Intell. 10, 92–105 (1988)

    Article  Google Scholar 

  40. Srinivasn, K., Dastor, P.H., Radhakrishnaiah, P., Jayaraman, S.: FDAS: a knowledge-based frame detection work for analysis of defects in woven textile structures. J. Text. Inst. 83(3), 431–447 (1992)

    Article  Google Scholar 

  41. Stojanovic, R., Mitropulos, P., Koulamas, C., Karayiannis, Y., Koubias, S., Papadopoulos, G.: Real-time vision-based system for textile fabric inspection. Real-Time Imag. 7(6), 507–518 (2001)

    Article  MATH  Google Scholar 

  42. Thomas, T., Cattoen, M.: Automatic inspection of simply patterned materialsin the textile industry. Proc. SPIE 2183, 2–12 (1995)

    Article  Google Scholar 

  43. Thomas, T.R., Rose’n, B.-G., Amini, N.: Fractal characterisation of the anisotropy of rough surfaces. Wear 232, 41–50 (1999)

    Article  Google Scholar 

  44. Thomas, T.R.: Rough Surfaces. Imperial College Press, London (1999)

    Google Scholar 

  45. Tsai, I., Lin, C., Lin, J.: Applying an artificial neural network to pattern recognition in fabric defects. Text. Res. J. 65(3), 123–130 (1995)

    Article  Google Scholar 

  46. Whitehouse, D.J.: Fractal or fiction. Wear 249, 345–353 (2001)

    Article  Google Scholar 

  47. Whitehouse, D.J.: Some theoretical aspects of structure functions, fractal parameters and related subjects. Proc. Inst. Mech. Eng. 215(J), 207–210 (2001)

    Article  Google Scholar 

  48. Yang, X.Z., Pang, G., Yung, N.: Fabric defect detection using adaptive wavelet. Proc. IEEE ICASSP 6, 3697–3700 (2001)

    Google Scholar 

  49. Yang, X.Z., Pang, G., Yung”, N.: Discriminative Fabric defect detection using adaptive wavelet. Opt. Eng. 41(12), 3116–3126 (2002)

    Article  Google Scholar 

  50. Yang, X.Z., Pang, G., Yung, N.: Discriminative training approaches to fabric defect classification based on wavelet transform. Pattern Recognit. 37(5), 889–899 (2004)

    Article  Google Scholar 

  51. Zeng, P., Hirata, T.: On-loom fabric inspection using multi-scale differentiation filtering. In: Proceedings of Industry Applications Society Conference, 37th IAS Annual Meeting. vol. 1, pp. 320–326. Pittsburg, PA, 13–18 Oct (2002)

  52. Zhang, Y.F., Barsee, R.R.: Fabric defect detection and classification using image analysis. Text. Res. J. 65(1), 1–9 (1995)

    Article  Google Scholar 

  53. Zucker, S.W., Terzopoulos, D.: Finding structure in co-occurrence matrices for texture analysis. In: Rosenfeld, A. (ed.) Image Modeling, pp. 423–445. Academic, New York (1990)

    Google Scholar 

Download references

Acknowledgments

The authors are highly thankful to Riby Abraham, Mechanical Engineering Department, I.I.T. Delhi, for introducing the structure function and for rendering the programming support.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Madasu Hanmandlu.

Appendix 1: Computation of topothesy fractal dimension (fractal parameters) features

Appendix 1: Computation of topothesy fractal dimension (fractal parameters) features

Let us take an image of size \(8 \times 8\)

$$\begin{aligned} I&= [255\; 254 \; 253\; 241\; 240 \; 239\; 238 \; 245;\\&255\; 254\; 255\; 251\; 254\; 255 \; 255 \;248;\\&239 \; 250 \; 234 \; 223 \; 251 \; 236 \; 223 \; 222;\\&245\; 248 \; 234 \; 241 \; 255 \; 235 \; 232 \; 248;\\&255 \; 243 \; 255 \; 255 \; 253 \; 236 \; 255 \; 254;\\&230 \; 226 \; 255 \; 255 \; 234 \; 230 \; 254 \; 238;\\&241 \; 241 \; 255 \; 248 \; 248 \; 252 \; 255 \; 233;\\&254 \; 255 \; 255 \; 253 \; 254 \; 254 \; 253 \; 255]; \end{aligned}$$

Assume a window size of 3. Then we will have the number of sub-images \(=\) floor (8/3) \(\times \) floor (8/3) \(=\) 4, i. e., \(B1,B2,B3\) and \(B4\).

$$\begin{aligned} \begin{array}{ll} B1=I(1:3,1:3) &{}B2=I(1:3,4:6)\\ B3=I(4:6,1:3) &{}B4=I(4:6,4:6) \end{array} \end{aligned}$$

The sub-images are as follows:

$$\begin{aligned} B1&= [255\; 254\; 253;\\&255\; 254\; 255;\\&239\; 250\; 234];\\ B2&= [241\; 240 \;239;\\&251\; 254\; 255;\\&223\; 251\; 236];\\ B3&= [245\; 248\; 234;\\&255\; 243\; 255;\\&230\; 226\; 236]\\ B4&= [241\; 255\; 235;\\&255\; 253\; 236;\\&255\; 234\; 230] \end{aligned}$$

We will get two features from a sub-image and thus have \(2 \times 4=8\) features for the image I of size \(8 \times 8\). The computation of structure function \(S1\) of \(B1\) is done using the following loops:

$$\begin{aligned}&\hbox {for}\,\tau = 1\,\hbox {to}\,3-1\,\hbox {do}\\&S1(\tau , \tau ) = 0;\\&\quad \hbox {for}\,i= 1\,\hbox {to}\,3-\tau \,\hbox {do}\\&\qquad \hbox {for}\, j= 1\,\hbox {to}\,3-\tau \,\hbox {do}\\&S1(\tau , \tau ) = S1(\tau , \tau )+{\vert }B1(i, j) - B1(i+\tau , j + \tau ) {\vert };\\&\hbox {End j};\\&\hbox {End i};\\&\hbox {End} \uptau ; \end{aligned}$$

From the above loops, we compute the elements of the structure function as follows:

For \(\tau = 1\);

$$\begin{aligned} S1(1,1)\!&= \!{\vert }B1(1,1)\!-\!B1(2,2){\vert } \!+\!{\vert }B1(1,2)\!-\!B1(2,3){\vert }\\&+\, {\vert }B1(2,1)\!-\!B1(3,2){\vert } \!+\! {\vert }B1(2,2)\!-\!B1(3,3){\vert }\\ S1(1,1)\!&= \!{\vert }255-254{\vert } \!+\! {\vert }254-255{\vert } \!+\! {\vert }255-250{\vert }\\&+\,{\vert }254-234{\vert } \!=\!1\!+\!1\!+\!5\!+\! 20 \!=\! 27\\ S1(1)\!&= \!S1(1,1)\!=\! 27/4\!=\!6.75 \quad // \quad \hbox {since } (m\!-\!\tau ) \times (n\!-\!\tau )\\ \!&= \! (3-1) \times (3-1) = 4 \end{aligned}$$

For\(\,\tau = 2\);

$$\begin{aligned}&S1(2,2)= {\vert }B1(2,2)-B1(3,3){\vert }= {\vert }255-234{\vert } =21\\&S1 (2)= S1(2, 2)= 21/1;\quad // \quad \hbox {since } (m-\tau ) \times (n-\tau )\\&\quad = (3-2)\times (3-2)=1 \end{aligned}$$

The values of \(\tau \) and \(S(\tau )\) and the corresponding values of \(\hbox {log}(\tau )\) and \(\hbox {Log} (S(\tau ))\) are listed here.

$$\begin{aligned} \begin{array}{l@{\quad }l@{\quad }l@{\quad }l} \tau &{}1&{} 2\\ \hbox {log}(\tau ) &{}0&{} 0.693\\ S(\tau ) &{}6.75&{} 21\\ \hbox {Log}(S(\tau )) &{}1.9095&{} 3.044 \end{array} \end{aligned}$$

Curve fitting by the equation \(y=mx+c\) gives slope = 1.6374 and the intercept \(c=1.9095\).

So the fractal dimension \((D) = 2-(\hbox {m}/2) = 2-(1.6374/2) =2-0.8187 =1.1813\).

Topothesy \((\wedge ) = \hbox {exp} (\hbox {c}/(2D-2)) = \hbox {exp}(1.9095/(2\times 1.1813-2)) =193.770\)

The above two fractal parameters are contributed by the first sub-image, i.e., B1. Similarly, the fractal parameters \(D\) and \(\wedge \) of \(B2\) are: \(D=2.6008, \wedge =2.1444\); of \(B3\) are \(D=2.161, \wedge =2,9677\) and of \(B4\) are: \(D=2.3847, \wedge =2.8818\).

After concatenating all the feature values of B1, B2, B3 and B4, we obtain the feature vector of size, 1 \(\times \) 8 as \((1.1813, 195.77, 2.6008, 2.1444, 2.1610, 2.9677, 2.3847, 2. 8818)\).

Rights and permissions

Reprints and permissions

About this article

Cite this article

Hanmandlu, M., Choudhury, D. & Dash, S. Detection of defects in fabrics using topothesy fractal dimension features. SIViP 9, 1521–1530 (2015). https://doi.org/10.1007/s11760-013-0604-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-013-0604-5

Keywords

Navigation