Memory management for Union-Find algorithms

  • Christophe Fiorio
  • Jens Gustedt
Algorithms II
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1200)


We provide a general tool to improve the real time performance of a broad class of Union-Find algorithms. This is done by minimizing the random access memory that is used and thus to avoid the well-known von Neumann bottleneck of synchronizing CPU and memory. A main application to image segmentation algorithms is demonstrated where the real time performance is drastically improved.


Image Segmentation Active Element Real Time Performance Random Access Memory Memory Management 
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.


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  1. 1.
    L. Banachowski, A complement to Tarjan 's result about the lower bound on the complexity of the set union problem, Inform. Process. Lett., 11 (1980), pp. 59–65.CrossRefGoogle Scholar
  2. 2.
    M. B. Dillencourt, H. Samet, and M. Tamminen, A general approach to connected-component labeling for arbitrary image representations, J. Assoc. Comput. Mach., 39 (1992), pp. 253–280. Corr p. 985–986.MathSciNetGoogle Scholar
  3. 3.
    C. Fiorio and J. Gustedt, Two linear time Union-Find strategies for image processing, Theoret. Comput. Sci., 154 (1996), pp. 165–181.CrossRefGoogle Scholar
  4. 4.
    -, Volume segmentation of 3-dimensional images, Tech. Rep. 515/1996, Technische Universität Berlin, 1996.Google Scholar
  5. 5.
    J. Gustedt, Efficient union-find for planar graphs and other sparse graph classes, in Graph-Theoretic Concepts in Computer Science, 22nd International Workshop WG '96, Ausiello et al., eds., Lecture Notes in Computer Science, Springer-Verlag, 1996, pp. 181–195. to appear.Google Scholar
  6. 6.
    J. A. La Poutré, New techniques for the union-find problem, in Proceedings of the first annual ACM-SIAM Symposium on Discrete Algorithms, A. Aggarwal et al., eds., Society of Industrial and Applied Mathematics (SIAM), 1990, pp. 54–63.Google Scholar
  7. 7.
    K. Mehlhorn, Data Structures and Algorithms 1: Sorting and Searching, Springer, 1984.Google Scholar
  8. 8.
    K. Mehlhorn and A. Tsakalidis, Handbook of Theoretical Computer Science, vol. A, Algorithms and Complexity, Elsevier Science Publishers B.V., Amsterdam, 1990, ch. 6, Data Structures, pp. 301–314.Google Scholar
  9. 9.
    J. Muerle and D. Allen, Experimental evaluation of techniques for automatic segmentation of objects in a complex scene, in Pictorial Pattern Recognition, G. C. Cheng et al., eds., Thompson, Washington, 1968, pp. 3–13.Google Scholar
  10. 10.
    N. Robertson and P. Seymour, Graph minors I, excluding a forest, J. Combin. Theory Ser. B, 35 (1983), pp. 39–61.Google Scholar
  11. 11.
    R. E. Tarjan, Efficiency of a good but not linear set union algorithm, J. Assoc. Comput. Mach., 22 (1975), pp. 215–225.Google Scholar
  12. 12.
    -, A class of algorithms which require non-linear time to maintain disjoint sets, J. Comput. System Sci., 18 (1979), pp. 110–127.CrossRefGoogle Scholar
  13. 13.
    R. E. Tarjan and J. van Leeuwen, Worst-case analysis of set union algorithms, J. Assoc. Comput. Mach., 31 (1984), pp. 245–281.MathSciNetGoogle Scholar
  14. 14.
    M. J. van Kreveld and M. H. Overmars, Union-copy structures and dynamic segment trees, J. of the Association for Computing Machinery, 40 (1993), pp. 635–652.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Christophe Fiorio
    • 1
  • Jens Gustedt
    • 1
  1. 1.Technische Universität BerlinBerlinGermany

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