[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

A combinational algorithm for connected-component labeling and Euler number computing

  • Original Research Paper
  • Published:
Journal of Real-Time Image Processing Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

Connected-component labeling and Euler number computing are two essential processing tasks for extracting objects’ features in a binary image for the pattern recognition, image analysis, and computer (robot) vision. In general, the two processing tasks are usually executed independently by different algorithms in different scans. This paper proposes a combinational algorithm for labeling connected components in a binary image and computing the Euler number of the image simultaneously. In our algorithm, for the current pixel, the two processing tasks use the same information obtained from its neighbor pixels in the same scan. Moreover, the information obtained during processing the current pixel will be used for processing the next pixel. Our method is simple in principle and powerful in practice. Experimental results demonstrated that our method is much more efficient than conventional methods on various kinds of images, either in the case where the Euler number is calculated alone or in the case where both connected-component labeling and the Euler number computing are necessary.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

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

Similar content being viewed by others

Notes

  1. http://www.mathworks.com/access/helpdesk/help/toolbox/images/bweuler.html.

  2. For an N × M-size binary image, a safe value of G is N × M/4.

  3. http://sampl.ece.ohio-state.edu/data/stills/sidba/index.htm.

  4. http://sipi.usc.edu/database/.

  5. http://www1.cs.columbia.edu/CAVE/software/curet/.

References

  1. Gonzalez, R.C., Woods, R.E.: Digital Image Processing, Third ed. edn, p. 07458. Pearson Prentice Hall, Upper Saddle River (2008)

    Google Scholar 

  2. Hashizume, A., Suzuki, R., Yokouchi, H., et al.: An algorithm of automated RBC classification and its evaluation. Jpn. J. Med. Electron. Bio. Eng. 28(1), 25–32 (1990) (in Japanese)

  3. Srihari S.N. Document image understanding. In: Proceedings ACM/IEEE Joint Fall Computer Conference, Dallas, TX, November, pp.87–95 (1986)

  4. Rosin P.L., Ellis T. Image difference Threshold strategies and shadow detection. In: Proceedings British Machine Vision Conference September, pp. 347–356 (1995)

  5. Nayar, S.K., Bolle, R.M.: Reflectance-based object recognition. Int. J. Comput. Vision 17(3), 219–240 (1996)

    Article  Google Scholar 

  6. Horn, B.P.K.: Robot Vision, pp. 73–77. McGraw-Hill, New York (1986)

    Google Scholar 

  7. Rosenfeld, A., Pfalts, J.L.: Sequential operations in digital picture processing. J. ACM 13(4), 471–494 (1996)

    Google Scholar 

  8. He, L., Chao, Y., Suzuki, K., Wu, K.: Fast connected-component labeling. Pattern Recogn. 42(9), 1977–1987 (2009)

    Article  MATH  Google Scholar 

  9. He, L., Chao, Y., Suzuki, K.: An efficient first-scan method for label-equivalence-based labeling algorithms. Pattern Recogn. Lett. 31(1), 28–35 (2010)

    Article  Google Scholar 

  10. He, L., Chao, Y., Suzuki, K.A.: Run-based two-scan labeling algorithm. IEEE Trans. Image Process. 17(5), 749–756 (2008)

    Article  MathSciNet  Google Scholar 

  11. Wu, K., Otoo, E., Suzuki, K.: Optimizing two-pass connected-component labeling algorithms. Pattern Anal. Appl. 12(2), 117–135 (2009)

    Article  MathSciNet  Google Scholar 

  12. Grana, C., Borghesani, D., Cucchiara, R.: Optimized block-based connected components labeling with decision trees. IEEE Trans. Image Process. 19(6), 1596–1609 (2010)

    Article  MATH  MathSciNet  Google Scholar 

  13. Chang, F., Chen, C.J., Lu, C.J.: A linear-time component-labeling algorithm using contour tracing technique. Comput. Vis. Image Underst. 93, 206–220 (2004)

    Article  Google Scholar 

  14. Hu, Q., Qian, G., Nowinski, W.L.: Fast connected-component labeling in three-dimensional binary images based on iterative recursion. Comput. Vis. Image Underst. 99, 414–434 (2005)

    Article  Google Scholar 

  15. Wolfe C., Nicholas T.C., Graham T., Pape J.A. Seeing through the fog: an algorithm for fast and accurate touch detection in optical tabletop surfaces. In: ACM International Conference on Interactive Tabletops and Surfaces (ITS ‘10), New York, NY, USA, November 2010, pp. 73–82

  16. Abramov, A., Kulvicius, T., Wörgötter, F., Dellen, B.: Real-time image segmentation on a GPU facing the multicore-challenge. In: Keller, R., Kramer, D., Weiss, J.P. (eds.) Lecture Notes in Computer Science, 6310, pp. 131–142. Springer, Berlin (2011)

    Google Scholar 

  17. Chen, M.H., Yan, P.F.: A fast algorithm to calculate the Euler number for binary image. Pattern Recogn. Lett. 8(5), 295–297 (1988)

    Article  MATH  Google Scholar 

  18. Juan, L.D.S., Juan, H.S.: On the computation of the Euler number of a binary object. Pattern Recogn. 29(3), 471–476 (1996)

    Article  Google Scholar 

  19. Zenzo, S., Cinque, L., Levialdi, S.: Run-based algorithms for binary image analysis and processing. IEEE Trans. PAMI 18(1), 83–89 (1996)

    Article  Google Scholar 

  20. Gray, S.B.: Local properties of binary images in two dimensions. IEEE Trans. Comput. 20, 551–561 (1971)

    Article  MATH  Google Scholar 

  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)

    Article  MATH  MathSciNet  Google Scholar 

  22. Otsu, N.A.: Threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybernet. 9, 62–66 (1979)

    Article  Google Scholar 

Download references

Acknowledgments

We thank the anonymous referees for their valuable comments that improved this paper greatly. We are grateful to the associate editor for his/her kind cooperation. This work was supported in part by the Grant-in-Aid for Scientific Research (C) of the Ministry of Education, Science, Sports and Culture of Japan under Grant No. 26330200, the Grant-in-Aid for Social Development Research Project of the Department of Science and Technology of Shaanxi Province of China under No. 2014K11-02-01-13, and the Grant-in-Aid for Scientific Research of Administration of Science and Technology of Xi’an of China under Grant No. CXY1343.

Author information

Authors and Affiliations

  1. Artificial Intelligence Institute, College of Electrical and Information Engineering, Shaanxi University of Science and Technology, Xi’an, 710021, Shaanxi, China

    Lifeng He, Xiao Zhao, Bin Yao & Yun Yang

  2. Faculty of Information Science and Technology, Aichi Prefectural University, Nagakute, Aichi, 4801198, Japan

    Lifeng He

  3. Faculty of Environment, Information and Business, Nagoya Sangyo University, Owariasahi, Aichi, 4888711, Japan

    Yuyan Chao

  4. School of Computer Science and Engineering, Xi’an University of Technology, Xi’an, 710048, China

    Zhenghao Shi

  5. Department of Radiology, The University of Chicago, Chicago, IL, 60637, USA

    Kenji Suzuki

Authors
  1. Lifeng He
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiao Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bin Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yun Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yuyan Chao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Zhenghao Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Kenji Suzuki
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lifeng He.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

He, L., Zhao, X., Yao, B. et al. A combinational algorithm for connected-component labeling and Euler number computing. J Real-Time Image Proc 13, 703–712 (2017). https://doi.org/10.1007/s11554-014-0433-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11554-014-0433-y

Keywords

Search

Navigation