Clique-to-clique distance computation using a specific architecture

  • J. Climent
  • A. Grau
  • J. Aranda
  • A. Sanfeliu
Poster Papers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1451)


In this paper, we present a new fast architecture to compute the distance between cliques in different graphs. The distance obtained is used as a support function for graph labelling using probabilistic relaxation techniques. The architecture presented consists on a pipe-lined structure which computes the distance between an input clique and k reference cliques. The number of processing elements needed is m2, and the number of cycles required to compute the distance is ni (being m the number of external nodes in the input clique, and ni the number of external nodes in the i-th reference clique). The processing elements are very simple basic cells and very simple communication between them is needed, which makes it suitable for VLSI implementation.


Incremental Cost Processing Element Clock Cycle Input Sequence Support Function 
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  1. [BUN]
    H. Bunke. “String Matching for Structural Pattern Recognition”. H.Bunke and A.Sanfeliu Eds, Syntactic and Structural Pattern Recognition. Theroy and Applications, pp.381–414, World Scientific, 1990.Google Scholar
  2. [CHE]
    H.D. Cheng, and K.S. Fu. “VLSI Architectures for String Matching and Pattern Matching”. Pattern Recognition, vol.20, n. 1, pp. 125–141. 1987.CrossRefGoogle Scholar
  3. [GRE]
    J.Gregor, and M.G. Thomason. “Efficient Dynamic Programming Alignment of Cyclic Strings by Shift Elimination”. Pattern recognition, vol.29, n.7, pp. 1179–1185, 1996.CrossRefGoogle Scholar
  4. [HUM]
    R.A. Hummel, and S.W. Zucker. “On the Foundations of Relaxation Labelling Processes”. IEEE Trans. Pattern Anal. Mach. Intell. Vo1.5, N.3, pp. 267–286. 1983.Google Scholar
  5. [KIT]
    J.Kittler, W.C.Christmas, M.Petrou. “Probabilistic Relaxation for Maching of Symbolic Structures”. Advances in structural and syntactic pattern recognition. Ed. by H. Bunke pp. 471–480.1995.Google Scholar
  6. [LIP]
    R.J. Lipton, and D. Lopresti. “A Systolic Array for Rapid String Comparison”. 1985 Chapel Hill Conf. on VLSI, H. Fuchs, de., Rockville, Md.: Computer Science Press, pp. 363–376. 1985.Google Scholar
  7. [LOP]
    D. Lopestri. “P-NAC: A Systolic array for comparing nucleic acid sequences”. Computer, vol.20, pp. 98–99. 1987.Google Scholar
  8. [MAE]
    M. Maes. “On a Cyclic String-to-String Correction Problem”. Information Proc. Letters, 35, pp.73–78. June 1990.CrossRefGoogle Scholar
  9. [SAS]
    R.Sastry, N. Ranganathan, and K. Remedios. “CASM:: A VLSI Chip for Approximate String Matching”. IEEE Trans. Pattern Anal. Mach. Intell. Vol. 17, N.8, pp. 824–830. 1995.CrossRefGoogle Scholar
  10. [SER]
    F. Serratosa and A. Sanfeliu. “Function-Described Graphs Applied to 3D Object Representation”.Image Analysis and Processing, proc. 9th International Conference ICIAP, Florence, pp. 701–708,1997.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • J. Climent
    • 1
  • A. Grau
    • 1
  • J. Aranda
    • 1
  • A. Sanfeliu
    • 2
  1. 1.Automatic Control and Computer Engineering DepartmentUniversitat Politècnica de Catalunya(UPC)UK
  2. 2.Institut de Robòtica i Informática IndustrialUK

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