A Novel Two-Scan Connected-Component Labeling Algorithm

  • Lifeng He
  • Yuyan Chao
  • Yun Yang
  • Sihui Li
  • Xiao Zhao
  • Kenji Suzuki
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 229)


This chapter proposes a novel two-scan labeling algorithm. In the first scan, all conventional two-scan labeling algorithms process image lines one by one, assigning each foreground pixel a provisional label, finding and resolving label equivalences between provisional labels. In comparison, our proposed method first scans image lines every four lines, assigns provisional labels to foreground pixels among each three lines, and finds and resolves label equivalences among those provisional labels. Then, it processes the leaving lines from top to bottom one by one, and for each line, assigns provisional labels to foreground pixels on the line, and finds and resolves label equivalences among the provisional labels and those assigned to the foreground pixels on the lines immediately above and below the current line. With our method, the average number of times for checking pixels for processing a foreground pixel will decrease; thus, the efficiency of labeling can be improved. Experimental results demonstrated that our method was more efficient than conventional label-equivalence-based labeling algorithms.


Computer vision Connected component Fast algorithm Label equivalence Labeling Pattern recognition 



This work was supported in part by the Ministry of Education, Science, Sports and Culture, Japan, Grant-in-Aid for Scientific Research (C), 23500222, 2011.


  1. 1.
    Alexey A, Tomas K, Florentin W, Babette D (2011) Real-time image segmentation on a GPU. Facing Multicore Chall Lect Notes Comput Sci 6310:131–142Google Scholar
  2. 2.
    Ballard DH (1982) Computer vision. Prentice-Hall, EnglewoodGoogle Scholar
  3. 3.
    Chang F, Chen CJ, Lu CJ (2004) A linear-time component-labeling algorithm using contour tracing technique. Comput Vis Image Underst 93:206–220CrossRefGoogle Scholar
  4. 4.
    Christopher W, Nicholas Graham TC, Pape JA (2010) 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). ACM, New York, USA, pp 73–82 (2010)Google Scholar
  5. 5.
    Dellen B, Erdal EA, Wrgtter F (2009) Segment tracking via a spatiotemporal linking process including feedback stabilization in an n-D lattice model. Sensors 9(11):9355–9379CrossRefGoogle Scholar
  6. 6.
    Gonzalez RC, Woods RE (1992) Digital image processing. Addison Wesley, ReadingGoogle Scholar
  7. 7.
    Haralick RM (1981) Some neighborhood operations. In: Real time/parallel computing image analysis. Plenum Press, New York, pp 11–35Google Scholar
  8. 8.
    Haralick RM, Shapiro LG (1992) Computer and robot vision I. Addison-Wesley, Reading, pp 28–48Google Scholar
  9. 9.
    Hashizume A, Suzuki R, Yokouchi H et al (1990) An algorithm of automated RBC classification and its evaluation. Bio Med Eng 28(1):25–32Google Scholar
  10. 10.
    He L, Chao Y, Suzuki K (2007) A linear-time two-scan labeling algorithm. In: 2007 IEEE international conference on image processing (ICIP). San Antonio, Texas, USA, pp V-241-V-244Google Scholar
  11. 11.
    He L, Chao Y, Suzuki K (2008) A run-based two-scan labeling algorithm. IEEE Trans Image Process 17(5):749–756MathSciNetCrossRefGoogle Scholar
  12. 12.
    He L, Chao Y, Suzuki K, Wu K (2009) Fast connected-component labeling. Pattern Recognit 42:1977–1987MATHCrossRefGoogle Scholar
  13. 13.
    He L, Chao Y, Suzuki K (2010) An efficient first-scan method for label-equivalence-based labeling algorithms. Pattern Recognit Lett 31:28–35CrossRefGoogle Scholar
  14. 14.
    He L, Chao Y, Suzuki K (2011) A run-based one-and-a-half-scan connected-component labeling algorithm. Int J Pattern Recognit Artif Intell 24(4):557–579CrossRefGoogle Scholar
  15. 15.
    He L, Chao Y, Suzuki K (2011) Two efficient label-equivalence-based connected-component labeling algorithms for three-dimensional binary images. IEEE Trans Image Process 20(8):2122–2134MathSciNetCrossRefGoogle Scholar
  16. 16.
    He L, Chao Y, Suzuki K (2012) A new two-scan algorithm for labeling connected components in binary images. Lecture notes in engineering and computer science: proceedings of the world congress on engineering 2012, WCE, 4–6 July 2012 U.K , London, pp 1141–1146Google Scholar
  17. 17.
    Hu Q, Qian G, Nowinski WL (2005) Fast connected-component labeling in three-dimensional binary images based on iterative recursion. Comput Vis Image Underst 99:414–434CrossRefGoogle Scholar
  18. 18.
    Lumia R, Shapiro L, Zungia O (1983) A new connected components algorithm for virtual memory computers. Comput Vis Graph Image Process 22(2):287–300CrossRefGoogle Scholar
  19. 19.
    Naoi S (1995) High-speed labeling method using adaptive variable window size for character shape feature. IEEE Asian Conf Comput Vis 1:408–411Google Scholar
  20. 20.
    Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9:62–66CrossRefGoogle Scholar
  21. 21.
    Ronsen C, Denjiver PA (1984) Connected components in binary images: the detection problem. Research Studies Press, New YorkGoogle Scholar
  22. 22.
    Rosenfeld A, Pfalts JL (1966) Sequential operations in digital picture processing. J ACM 13(4):471–494MATHCrossRefGoogle Scholar
  23. 23.
    Samet H (1984) The quadtree and related hierarchical data structures. Comput Surv 16(2):187–260Google Scholar
  24. 24.
    Shima Y, Murakami T, Koga M, Yashiro H, Fujisawa H (1990) A high-speed algorithm for propagation-type labeling based on block sorting of runs in binary images. In: Proceedings of 10th international conference pattern recognition, pp 655–658Google Scholar
  25. 25.
    Suzuki K, Horiba I, Sugie N (2003) Linear-time connected-component labeling based on sequential local operations. Comput Vis Image Underst 89:1–23MATHCrossRefGoogle Scholar
  26. 26.
    Udupa JK, Ajjanagadde VG (1990) Boundary and object labeling in three-dimensional images. Comput Vis Graph Image Process 51(3):355–369CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Lifeng He
    • 1
    • 2
  • Yuyan Chao
    • 3
  • Yun Yang
    • 1
  • Sihui Li
    • 1
  • Xiao Zhao
    • 1
  • Kenji Suzuki
    • 4
  1. 1.College of Electrical and Information EngineeringShaanxi University of Science and TechnologyXi’anChina
  2. 2.Faculty of Information Science and TechnologyAichi Profectural UniversityNagakuteJapan
  3. 3.Graduate School of Environment ManagementNagoya Sangyo UniversityOwariasahiJapan
  4. 4.Department of Radiology, Division of the Biological SciencesThe University of ChicagoChicagoUSA

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