Skip to main content

Advertisement

SpringerLink
Log in

A new feature extraction process based on SFTA and DWT to enhance classification of ceramic tiles quality

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

Abstract

We propose a combination of image processing methods to detect ceramic tiles defects automatically. The primary goal is to identify faults in ceramic tiles, with or without texture. The process consists of four steps: preprocessing, feature extraction, optimization, and classification. In the second step, gray-level co-occurrence matrix, segmentation-based fractal texture analysis, discrete wavelet transform, local binary pattern, and a novel method composed of segmentation-based fractal texture analysis and discrete wavelet transform are applied. The genetic algorithm was used to optimize the parameters. In the classification step, k-nearest neighbor, support vector machine, multilayer perceptron, probabilistic neural network, and radial basis function network were assessed. Two datasets were used to validate the proposed process, totaling 782 ceramic tiles. In comparison with the other feature extraction methods commonly used, we demonstrate that the use of SFTA with DWT had a remarkable increase in the overall accuracy, without compromising computational time. The proposed method can be executed in real time on actual production lines and reaches a defect detection accuracy of 99.01% for smooth tiles and 97.89% for textured ones.

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.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. The datasets are available in https://goo.gl/aTnBQE.

References

  1. Beattie, J.: Automatic inspection in the glass industry. Automatica 11(1), 93 (1975)

    Article  Google Scholar 

  2. Ragab, K., Alsharay, N.: In: 2017 IEEE 13th International Symposium on Autonomous Decentralized System (ISADS), pp. 255–261. IEEE (2017)

  3. Watpade, A.B., Amrutkar, M.S., Bagrecha, N.Y., Vaidya, A.: Qcuip: Quality control using image processing. Int. J. Eng. Res. Appl. 4, 15 (2014)

    Google Scholar 

  4. Elbehiery, H., Hefnawy, A., Elewa, M.: In: WEC (5), pp. 158–162 (2005)

  5. Meena, Y., Mittal, D.A.: Blobs & crack detection on PlaincCeramic tile surface. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 3(7) (2013)

  6. Sioma, A.: Automated control of surface defects on ceramic tiles using 3D image analysis. Materials 13(5), 1250 (2020)

    Article  Google Scholar 

  7. Kesharaju, M., Nagarajah, R.: Feature selection for neural network based defect classification of ceramic components using high frequency ultrasound. Ultrasonics 62, 271 (2015)

    Article  Google Scholar 

  8. Eren, E., Kurama, S., Solodov, I.: Characterization of porosity and defect imaging in ceramic tile using ultrasonic inspections. Ceram. Int. 38(3), 2145 (2012)

    Article  Google Scholar 

  9. Crouch, I.G., Kesharaju, M., Nagarajah, R.: Characterisation, significance and detection of manufacturing defects in reaction sintered silicon carbide armour materials. Ceram. Int. 41(9), 11581 (2015)

    Article  Google Scholar 

  10. Liu, J., Dai, Q., Chen, J., Chen, S., Ji, H., Dua, W., Deng, X., Wang, Z., Guo, G., Luo, H.: The two dimensional microstructure characterization of cemented carbides with an automatic image analysis process. Ceram. Int. 43(17), 14865 (2017)

    Article  Google Scholar 

  11. Liao, P.S., Chen, T.S., Chung, P.C., et al.: A fast algorithm for multilevel thresholding. J. Inf. Sci. Eng. 17(5), 713 (2001)

    Google Scholar 

  12. Mishra, R., Shukla, D.: An automated ceramic tiles defect detection and classification system based on artificial neural network. Int. J. Emerg. Technol. Adv. Eng 4(3), 229–233 (2014)

    Google Scholar 

  13. Rimac-Drlje, S., Keller, A., Hocenski, Z.: In: Proceedings of the IEEE International Symposium on Industrial Electronics. ISIE 2005, vol. 3, pp. 1255–1260. IEEE (2005)

  14. Ghazvini, M., Monadjemi, S., Movahhedinia, N., Jamshidi, K.: Defect detection of tiles using 2D-wavelet transform and statistical features. World Acad. Sci. Eng. Technol. 49, 901 (2009)

    Google Scholar 

  15. Mohan, V., Kumar, S.S.: An automated tiles defect detection. Int. J. Comput. Appl. 109(11), 24–27 (2015)

    Google Scholar 

  16. Novak, I., Hocenski, Z.: In: Proceedings of the IEEE International Symposium on Industrial Electronics. ISIE 2005, vol. 3, pp. 1279–1283. IEEE (2005)

  17. Sharma, M., Kaur, G.: Integrated approach for defect detection in ceramic tiles. Int. J. Comput. Technol. 3, 259–262 (2012)

    Article  Google Scholar 

  18. Macarini, L.A., Weber, T.: SIBGRAPI (2017)

  19. Xie, X., Mirmehdi, M.: TEXEMS: texture exemplars for defect detection on random textured surfaces. IEEE Trans. Pattern Anal. Mach. Intell. 29(8), 1454 (2007)

    Article  Google Scholar 

  20. Caulier, Y., Bourennane, S.: Visually inspecting specular surfaces: a generalized image capture and image description approach. IEEE Trans. Pattern Anal. Mach. Intell. 32(11), 2100 (2010)

    Article  Google Scholar 

  21. Lee, S.W.: Off-line recognition of totally unconstrained handwritten numerals using multilayer cluster neural network. IEEE Trans. Pattern Anal. Mach. Intell. 18(6), 648 (1996)

    Article  MathSciNet  Google Scholar 

  22. Randen, T., Husoy, J.H.: Filtering for texture classification: a comparative study. IEEE Trans. Pattern Anal. Mach. Intell. 21(4), 291 (1999)

    Article  Google Scholar 

  23. B.A. of Technical Standards, NBR 13818—Ceramic plates for coating—Specification and test methods (1997)

  24. de L’Eclairag, C.I.: CIE 015:2004: Colorimetry (Commission Internationale de L’Eclairag, 2004)

  25. Arthur, D., Vassilvitskii, S.: In: Proceedings of the 18th Annual ACM-SIAM Symposium on Discrete Algorithms. Society for Industrial and Applied Mathematics, Philadelphia, PA, USA, SODA ’07, pp. 1027–1035 (2007)

  26. Haralick, R.M., Shanmugam, K., et al.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 6, 610 (1973)

    Article  Google Scholar 

  27. Soh, L.K., Tsatsoulis, C.: Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices. IEEE Trans. Geosci. Remote Sens. 37(2), 780 (1999). https://doi.org/10.1109/36.752194

    Article  Google Scholar 

  28. Gomez, W., Pereira, W.C.A., Infantosi, A.F.C.: Analysis of co-occurrence texture statistics as a function of gray-level quantization for classifying breast ultrasound. IEEE Trans. Med. Imaging 31(10), 1889 (2012). https://doi.org/10.1109/TMI.2012.2206398

    Article  Google Scholar 

  29. Costa, A.F., Humpire-Mamani, G., Traina, A.J.M.: In: 2012 25th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 39–46. IEEE (2012)

  30. Stanković, R.S., Falkowski, B.J.: The Haar wavelet transform: its status and achievements. Comput. Electr. Eng. 29(1), 25 (2003)

    Article  Google Scholar 

  31. Shirazi, A.A., Dehghani, A., Farsi, H., Yazdi, M.: In: 2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA), pp. 258–261. IEEE (2017)

  32. Yu, Z., Chen, H., Liu, J., You, J., Leung, H., Han, G.: Hybrid k-nearest neighbor classifier. IEEE Trans. Cybern. 46(6), 1263 (2016)

    Article  Google Scholar 

  33. Friedman, J., Hastie, T., Tibshirani, R.: The Elements of Statistical Learning, vol. 1. Springer Series in Statistics New York (2001)

  34. Burges, C.J.: A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 2(2), 121 (1998)

    Article  Google Scholar 

  35. Akande, K.O., Owolabi, T.O., Twaha, S., Olatunji, S.O.: Performance comparison of SVM and ANN in predicting compressive strength of concrete. IOSR J. Comput. Eng. 16(5), 88 (2014)

    Article  Google Scholar 

  36. Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 1–27 (2011). https://doi.org/10.1145/1961189.1961199

  37. Marquardt, D.W.: The programme Simfit is a MSDOS compatible routine for non-linear regression analysis as described by Marquardt in. J. Soc. Ind. Appl. Math. 11(2), 431 (1963). https://doi.org/10.1137/0111030

    Article  Google Scholar 

  38. Specht, D.F.: Probabilistic neural networks. Neural Netw. 3(1), 109 (1990)

    Article  Google Scholar 

  39. Powell, M.J.D.: Radial Basis Functions for Multivariable Interpolation: A Review, pp. 143–167. Clarendon Press, New York (1987)

    MATH  Google Scholar 

  40. François, D., Wertz, V., Verleysen, M.: In: ESANN, pp. 239–244 (2006)

  41. Grefenstette, J.: Optimization of control parameters for genetic algorithms. IEEE Trans. Syst. Man Cybern. 16(1), 122 (1986). https://doi.org/10.1109/tsmc.1986.289288

    Article  Google Scholar 

  42. Pontius Jr., R.G., Millones, M.: Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment. Int. J. Remote Sens. 32(15), 4407 (2011)

    Article  Google Scholar 

  43. Viera, A.J., Garrett, J.M.: Understanding interobserver agreement: the kappa statistic. J. Fam. Med. 37, 360–363 (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computing, Federal University of Santa Catarina, Araranguá, Brazil

    Luan Casagrande, Luiz Antonio Buschetto Macarini & Daniel Bitencourt

  2. Computer Science Department, Federal University of Santa Catarina, Florianópolis, Brazil

    Antônio Augusto Fröhlich

  3. Information Science Department, Federal University of Santa Catarina, Florianópolis, Brazil

    Gustavo Medeiros de Araujo

Authors
  1. Luan Casagrande
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Luiz Antonio Buschetto Macarini
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Daniel Bitencourt
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Antônio Augusto Fröhlich
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Gustavo Medeiros de Araujo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Luiz Antonio Buschetto Macarini.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Casagrande, L., Macarini, L.A.B., Bitencourt, D. et al. A new feature extraction process based on SFTA and DWT to enhance classification of ceramic tiles quality. Machine Vision and Applications 31, 71 (2020). https://doi.org/10.1007/s00138-020-01121-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00138-020-01121-1

Keywords

Search

Navigation