Advertisement

Soft Computing

, Volume 22, Issue 13, pp 4197–4203 | Cite as

Automatic image thresholding using Otsu’s method and entropy weighting scheme for surface defect detection

  • Mai Thanh Nhat Truong
  • Sanghoon Kim
Focus
  • 325 Downloads

Abstract

Defect detection is one of the most important tasks and a challenging problem for industrial quality control. Among the available visual inspection techniques, automatic thresholding is a commonly used approach for defect detection because of the simplicity in terms of its implementation and computing. In this paper, we propose an automatic thresholding technique, which is an improvement in Otsu’s method, using an entropy weighting scheme. The proposed method enables the detection of extremely small defect regions compared to the product surface area. Experimental results confirm the efficiency of the proposed system over other techniques.

Keywords

Automatic thresholding Nondestructive testing Defect detection Entropy 

Notes

Acknowledgements

This study was funded by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2015R1D1A1A01057518).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. Al-Tairi ZH, Rahmat RW, Saripan MI, Sulaiman PS (2014) Skin segmentation using YUV and RGB color spaces. J Inf Process Syst 100(2):283–299CrossRefGoogle Scholar
  2. Asha V, Bhajantri N, Nagabhushan P (2012) Automatic detection of texture-defects using texture-periodicity and Jensen-Shannon divergence. J Inf Process Syst 80(2):359–374CrossRefGoogle Scholar
  3. Bhajantri N, Kumar RP, Nagabhushan P (2013) Discriminatory projection of camouflaged texture through line masks. J Inf Process Syst 90(4):660–677CrossRefGoogle Scholar
  4. Chaki N, Shaikh SH, Saeed K (2014) A Comprehensive survey on image binarization techniques. In: Kacprzyk J (ed) Exploring image binarization techniques. Springer, New Delhi, pp 5–15Google Scholar
  5. Fan JL, Lei B (2012) A modified valley-emphasis method for automatic thresholding. Pattern Recognit Lett 330(6):703–708CrossRefGoogle Scholar
  6. Gholizadeh S (2016) A review of non-destructive testing methods of composite materials. Proc Struct Integr 1:50–57CrossRefGoogle Scholar
  7. Hussain A, Abbasi AR, Afzulpurkar N (2012) Detecting & interpreting self-manipulating hand movements for student’s affect prediction. Human-centric Comput Inf Sci 20(1):1–18Google Scholar
  8. Kapur JN, Sahoo PK, Wong AKC (1985) A new method for gray-level picture thresholding using the entropy of the histogram. Comput Vis Graph Image Process 290(3):273–285CrossRefGoogle Scholar
  9. Liao PS, Chen TS, Chung PC (2001) A fast algorithm for multilevel thresholding. J Inf Sci Eng 170(5):713–727Google Scholar
  10. Liu Z, Wang J, Zhao Q, Li C (2014) A fabric defect detection algorithm based on improved valley-emphasis method. Res J Appl Sci Eng Technol 70(12):2427–2431Google Scholar
  11. Ng HF (2006) Automatic thresholding for defect detection. Pattern Recognit Lett 270(14):1644–1649CrossRefGoogle Scholar
  12. Ng HF, Jargalsaikhan D, Tsai H C, and Lin C Y (2013) An improved method for image thresholding based on the valley-emphasis method. In: Signal and information processing association annual summit and conference (APSIPA), 2013 Asia-Pacific, Kaohsiung, Taiwan. IEEE, pp 1–4Google Scholar
  13. Otsu N (1979) A threshold selection method from gray-level histogram. IEEE Trans Syst Man Cybern 90(1):62–66MathSciNetCrossRefGoogle Scholar
  14. Sezgin M, Sankur B (2004) Survey over image thresholding techniques and quantitative performance evaluation. J Electron Imaging 130(1):146–168Google Scholar
  15. Shankar N, Zhong Z (2005) Defect detection on semiconductor wafer surfaces. Microelectron Eng 770(3–4):337–346CrossRefGoogle Scholar
  16. Tolba AS, Raafat HM (2015) Multiscale image quality measures for defect detection in thin films. Int J Adv Manuf Technol 790(1):113–122CrossRefGoogle Scholar
  17. Uddin J, Islam R, Kim J-M (2014) Texture feature extraction techniques for fault diagnosis of induction motors. J Converg 50:15–20Google Scholar
  18. Verma O, Jain V, Gumber R (2013) Simple fuzzy rule based edge detection. J Inf Process Syst 90(4):575–591CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Department of Electrical, Electronics, and Control EngineeringHankyong National UniversityAnseong-siRepublic of Korea

Personalised recommendations