Enhancing CCL Algorithms by Using a Reduced Connectivity Mask

  • Uriel H. Hernandez-Belmonte
  • Victor Ayala-Ramirez
  • Raul E. Sanchez-Yanez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7914)

Abstract

In this paper, we propose to use a Reduced Connectivity Mask (RCM) in order to enhance connected component labeling (CCL) algorithms. We use the proposed RCM (a 2×2 spatial neighborhood) as the scanning window of a Union-Find labeling method and of a fast connected component labeling algorithm recently proposed in the literature. In both cases, the proposed mask enhances the performance of the algorithm with respect to the one exhibited by each algorithm in its original form. We have tested the two enhanced approaches proposed here against the fast connected component labeling algorithm proposed by He and the classical contour tracing algorithm. We have compared their execution time when labeling a set of uniform random noise test images of varying occupancy percentages. We show detailed results of all these tests. We also discuss the differences in behavior shown by the set of algorithms under test. The RCM variants exhibit a better performance than the previous CCL algorithms.

Keywords

Input Image Foreground Pixel Spatial Neighborhood Label Algorithm Pattern Recognition Letter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Asano, T., Tanaka, H.: In-place algorithm for connected components labeling. Journal of Pattern Recognition Research 5(1) (2010) Google Scholar
  2. Chang, F., Chen, C.-J.: A component-labeling algorithm using contour tracing technique. In: International Conference on Document Analysis and Recognition, pp. 741–745. IEEE Computer Society (2003)Google Scholar
  3. Chang, F., Chen, C.-J., Lu, C.-J.: A linear-time component-labeling algorithm using contour tracing technique. Computer Vision and Image Understanding 93(2), 206–220 (2004)CrossRefGoogle Scholar
  4. Di Stefano, L., Bulgarelli, A.: A simple and efficient connected components labeling algorithm. In: International Conference on Image Analysis and Processing, pp. 322–327 (1999)Google Scholar
  5. Dillencourt, M.B., Samet, H.: A general approach to connected-component labeling for arbitrary image representations. Journal of the ACM 39, 253–280 (1992)MathSciNetMATHCrossRefGoogle Scholar
  6. Fiorio, C., Gustedt, J.: Two linear time Union Find strategies for image processing. Theoretical Computer Science 154(2), 165–181 (1996)MathSciNetMATHCrossRefGoogle Scholar
  7. Greiner, J.: A Comparison of Data-Parallel Algorithms for Connected Components. In: Proceedings Symposium on Parallel Algorithms and Architectures, pp. 16–25 (1994)Google Scholar
  8. Han, Y., Wagner, R.A.: An efficient and fast parallel-connected component algorithm. Journal of the ACM 37(3), 626–642 (1990)MathSciNetMATHCrossRefGoogle Scholar
  9. Hardwick, J.: Practical Parallel Divide-and-Conquer Algorithms, PhD thesis, School of Computer Science, Carnegie Mellon University (1997)Google Scholar
  10. He, L., Chao, Y., Suzuki, K.: A linear-time two-scan labeling algorithm. In: IEEE International Conference on Image Processing, pp. 241–244 (2007)Google Scholar
  11. He, L., Chao, Y., Suzuki, K.: A run-based two-scan labeling algorithm. IEEE Transactions on Image Processing 17(5), 749–756 (2008)MathSciNetCrossRefGoogle Scholar
  12. He, L., Chao, Y., Suzuki, K.: An efficient first-scan method for label-equivalence-based labeling algorithms. Pattern Recognition Letters 31(1), 28–35 (2010a)CrossRefGoogle Scholar
  13. He, L., Chao, Y., Suzuki, K.: An efficient first-scan method for label-equivalence-based labeling algorithms. Pattern Recognition Letters 31, 27–35 (2010b)Google Scholar
  14. He, L., Chao, Y., Suzuki, K., Wu, K.: Fast connected-component labeling. Pattern Recognition 42(9), 1977–1987 (2009)MATHCrossRefGoogle Scholar
  15. Krishnamurthy, A., Lumetta, S.S., Culler, D.E., Yelick, K.: Connected components on distributed memory machines. In: Parallel Algorithms: Third DIMACS Implementation Challenge, pp. 1–21. American Mathematical Society (1994)Google Scholar
  16. Samet, H.: Connected component labeling using quadtrees. Journal of the ACM 28(3), 487–501 (1981)MathSciNetMATHCrossRefGoogle Scholar
  17. Samet, H., Tamminen, M.: Efficient component labeling of images of arbitrary dimension represented by linear bintrees. IEEE Transactions on Pattern Analysis and Machine Intelligence 10(4), 579–586 (1988)CrossRefGoogle Scholar
  18. Suzuki, K., Horiba, I., Sugie, N.: Linear-time connected-component labeling based on sequential local operations. Computer Vision and Image Understanding 89(1), 1–23 (2003)MATHCrossRefGoogle Scholar
  19. Wang, X., Davis, W.A.: Connected component labeling using modified linear quadtrees. In: Graphics Interface 1986, pp. 235–240 (1986)Google Scholar
  20. Wu, K., Otoo, E., Shoshani, A.: Optimizing connected component labeling algorithms. In: Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, vol. 5747, pp. 1965–1976 (2005)Google Scholar
  21. Wu, K., Otoo, E., Suzuki, K.: Two strategies to speed up connected component labeling algorithms, Tech Report LBNL-59102 (2005)Google Scholar
  22. Wu, K., Otoo, E., Suzuki, K.: Optimizing two-pass connected-component labeling algorithms. Pattern Analysis and Applications 12(2), 117–135 (2008)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Uriel H. Hernandez-Belmonte
    • 1
  • Victor Ayala-Ramirez
    • 1
  • Raul E. Sanchez-Yanez
    • 1
  1. 1.Universidad de Guanajuato DICISMexico

Personalised recommendations