Advertisement

Journal of Real-Time Image Processing

, Volume 15, Issue 4, pp 709–723 | Cite as

A fast algorithm for integrating connected-component labeling and euler number computation

  • Lifeng He
  • Bin Yao
  • Xiao Zhao
  • Yun Yang
  • Zhenghao Shi
  • Hideto Kasuya
  • Yuyan Chao
Original Research Paper
  • 131 Downloads

Abstract

This paper proposes a fast algorithm for integrating connected-component labeling and Euler number computation. Based on graph theory, the Euler number of a binary image in the proposed algorithm is calculated by counting the occurrences of four patterns of the mask for processing foreground pixels in the first scan of a connected-component labeling process, where these four patterns can be found directly without any additional calculation; thus, connected-component labeling and Euler number computation can be integrated more efficiently. Moreover, when computing the Euler number, unlike other conventional algorithms, the proposed algorithm does not need to process background pixels. Experimental results demonstrate that the proposed algorithm is much more efficient than conventional algorithms either for calculating the Euler number alone or simultaneously calculating the Euler number and labeling connected components.

Keywords

Graph theory Euler number Connected-component labeling Pattern recognition Computer vision 

Notes

Acknowledgments

We thank the anonymous referees for their valuable comments that greatly improved this paper. This work was supported in part by the National Natural Science Foundation of China under Grant No. 61471227, the Grant-in-Aid for Scientific Research (C) of the Ministry of Education, Science, Sports, and Culture of Japan under Grant No. 26330200, and the Key Science and Technology Program for Social Development of Shaanxi Province, China (Program No. 2014K11-02-01-13).

References

  1. 1.
    Gonzalez, R.C., Woods, R.E.: Digital image processing. Third ed., Pearson Prentice Hall, Upper Saddle River, NJ 07458 (2008)Google Scholar
  2. 2.
    Horn, B.P.K.: Robot Vision, pp. 73–77. McGraw-Hill, New York (1986)Google Scholar
  3. 3.
    Srihari, S.N.: Document image understanding. In: Proceedings ACM/IEEE Joint Fall Computer Conference, Dallas, TX, pp. 87–95 (1986)Google Scholar
  4. 4.
    Rosin, P.L., Ellis, T.: Image difference threshold strategies and shadow detection. In: Proceedings British Machine Vision Conference, pp. 347–356 (1995)Google Scholar
  5. 5.
    Nayar, S.K., Bolle, R.M.: Reflectance-based object recognition. Int. J. Comput. Vis. 17(3), 219–240 (1996)CrossRefGoogle Scholar
  6. 6.
    Rosenfeld, A., Pfalts, J.L.: Sequential operations in digital picture processing. J. ACM 13(4), 471–494 (1966)zbMATHCrossRefGoogle Scholar
  7. 7.
    Suzuki, K., Horiba, I., Sugie, N.: Linear-time connected-component labeling based on sequential local operations. Comput. Vis. Image Understand. 89, 1–23 (2003)zbMATHCrossRefGoogle Scholar
  8. 8.
    Gotoh, T., Ohta, Y., Yoshida, M., Shirai, Y.: Component labeling algorithm for video rate processing. Proc. SPIE 804, 217–224 (1987)CrossRefGoogle Scholar
  9. 9.
    Tarjan, R.E.: Efficiency of a good but not linear set union algorithm. J. ACM 22(2), 215–225 (1975)MathSciNetzbMATHCrossRefGoogle Scholar
  10. 10.
    He, L., Chao, Y., Suzuki, K.: A linear-time two-scan labeling algorithm. Image Processing, 2007. ICIP 2007. IEEE Int. Conf., pp. V-241–V-244, San Antonio (2007)Google Scholar
  11. 11.
    He, L., Chao, Y., K, Suzuki: A run-based two-scan labeling algorithm. IEEE Trans. Image Process. 17(5), 749–756 (2008)MathSciNetCrossRefGoogle Scholar
  12. 12.
    He, L., Chao, Y., Suzuki, K., Wu, K.: Fast Connected-Component Labeling. Patt. Recognit. 42(9), 1977–1987 (2009)zbMATHCrossRefGoogle Scholar
  13. 13.
    He, L., Chao, Y., Suzuki, K.: An efficient first-scan method for label-equivalence-based labeling algorithms. Patt. Recognit. Lett. 31(1), 28–35 (2010)CrossRefGoogle Scholar
  14. 14.
    Grana, C., Borghesani, D., Cucchiara, R.: Optimized Block-Based Connected Components Labeling With Decision Trees. IEEE Trans. Image Process. 19(6), 1596–1609 (2010)MathSciNetzbMATHCrossRefGoogle Scholar
  15. 15.
    Chang, F., Chen, C.J., Lu, C.J.: A linear-time component-labeling algorithm using contour tracing technique. Comput. Vis. Image Understand. 93, 206–220 (2004)CrossRefGoogle Scholar
  16. 16.
    Gray, S.B.: Local properties of binary images in two dimensions. IEEE Trans. Comput. C-20:551–561 (1971)Google Scholar
  17. 17.
  18. 18.
    Chen, M.H., Yan, P.F.: A fast algorithm to calculate the Euler number for binary image. Pattern Recognit. Lett. 8(5), 295–297 (1988)zbMATHCrossRefGoogle Scholar
  19. 19.
    Juan, L.D.S., Juan, H.S.: On the computation of the Euler number of a binary object. Pattern Recognit. 29(3), 471–476 (1996)CrossRefGoogle Scholar
  20. 20.
    Zenzo, S., Cinque, L., Levialdi, S.: Run-based algorithms for binary image analysis and processing. IEEE Trans. 18(1), 83–89 (1996)Google Scholar
  21. 21.
    He, L., Chao, Y., Suzuki, K.: An Algorithm for Connected-Component Labeling, Hole Labeling and Euler Number Computing. J. Comput. Sci. Technol. 28(3), 468–478 (2013)MathSciNetzbMATHCrossRefGoogle Scholar
  22. 22.
    He, L., Zhao, X., Yao, B., Yang, Y., Chao, Y., Shi, Z., Suzuki, K.A.: Combinational algorithm for connected-component labeling and euler number computing. J. Real Time Image Process. doi: 10.1007/s11554-014-0433-y
  23. 23.
    West, D.B.: Introduction to graph theory. Second edition, Prentice Hall (2001)Google Scholar
  24. 24.
    He, L., Yao, B., Zhao, X., Yan, Y., Chao, Y., Ohta A.: A graph-theory-based algorithm for euler number computing. IEICE TRANSACTIONS on Information and Systems. E98-D(2), 457–461 (2015)Google Scholar
  25. 25.
    Otsu, N.A.: threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybernet. 9, 62–66 (1979)CrossRefGoogle Scholar
  26. 26.
  27. 27.
  28. 28.

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Lifeng He
    • 1
    • 2
  • Bin Yao
    • 1
  • Xiao Zhao
    • 1
  • Yun Yang
    • 1
  • Zhenghao Shi
    • 3
  • Hideto Kasuya
    • 2
  • Yuyan Chao
    • 4
  1. 1.Artificial Intelligence Institute, College of Electrical and Information EngineeringShaanxi University of Science and TechnologyShaanxiChina
  2. 2.Faculty of Information Science and TechnologyAichi Prefectural UniversityAichiJapan
  3. 3.School of Computer Science and EngineeringXi’an University of TechnologyXi’anChina
  4. 4.Faculty of Environment, Information and BusinessNagoya Sangyo UniversityAichiJapan

Personalised recommendations