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Parallel low-level image processing on a distributed-memory system

  • Cristina Nicolescu
  • Pieter Jonker
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1800)

Abstract

The paper presents a method to integrate parallelism in the DIPLIB sequential image processing library. The library contains several framework functions for different types of operations. We parallelize the filter framework function (contains the neighborhood image processing operators). We validate our method by testing it with the geometric mean filter. Experiments on a cluster of workstations show linear speedup.

Keywords

Window Size Single Instruction Multiple Data Framework Function Output Pixel Slave Processor 
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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References

  1. 1.
    P. Challermvat, N. Alexandridis, P. Piamsa-Niga, M. O’Connell: Parallel image processing in heterogenous computing network systems, Proceedings of IEEE International Conference on Image Processing 16–19 sept. 1996, Lausanne, vol.3, pp.161–164CrossRefGoogle Scholar
  2. 2.
    J.G.E. Olk, P.P. Jonker: Parallel Image Processing Using Distributed Arrays of Buckets, Pattern recognition and Image Analysis, vol. 7, no. 1, pp.114–121, 1997Google Scholar
  3. 3.
    J.M. Squyres, A. Lumsdaine, R. Stevenson: A toolkit for parallel image processing, Proceedings of the SPIE Conference on Parallel and Distributed Methods for Image processing, San Diego, 1998Google Scholar
  4. 4.
    S.E. Umbaugh: Computer Vision and Image Processing-a practical approach using GVIPtools, Prentice Hall International Inc., 1998Google Scholar
  5. 5.
    I. Pitas: Parallel Algorithms for Digital Image Processing, Computer Vision and Neural Networks, John Wiley&Sons, 1993Google Scholar
  6. 6.
    K.L. Johnson, M.F. Kaashoek and D.A. Wallach: CRL: High-Performance All-Software Distributed Shared Memory, Proceedings of the Fifteenth Symposium on Operating Systems Principles, 1995Google Scholar
  7. 7.
    M. Snir, S. Otto, S. Huss, D. Walker and J. Dongarra: MPI-The Complete Reference, vol.1, The MPI Core, The MIT Press, 1998Google Scholar
  8. 8.
    R.A.F. Bhoedjang, T. Ruhl and H.E. Bal: Efficient Multicast on Myrinet Using Link-level Flow Control, Proceedings of International Conference on Parallel Processing, pp. 381–390, Minneapolis MN, 1998Google Scholar
  9. 9.
    T. Ruhl, H, Bal, R. Bhoedjang, K. Langendoen and G. Benson: Experience with a portability layer for implementing parallel programming systems, Proceedings of International Conference on Parallel and Distributed Processing Techniques and Applications, pp. 1477–1488, Sunnyvale CA, 1996Google Scholar
  10. 10.
    J.E. Lecky: How to optimize a machine vision application for MMX, Image Processing Europe, March Issue, pp. 16–20, 1999.Google Scholar
  11. 11.

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Cristina Nicolescu
    • 1
  • Pieter Jonker
    • 1
  1. 1.Faculty of Applied Physics Pattern Recognition GroupDelft University of TechnologyDelftThe Netherlands

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