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Using the ProSet-Linda prototyping language for investigating MIMD algorithms for model matching in 3-D computer vision

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Parallel Algorithms for Irregularly Structured Problems (IRREGULAR 1995)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 980))

Abstract

This paper discusses the development of algorithms for parallel interpretation-tree model matching for 3-D computer vision applications such as object recognition. The algorithms are developed with a prototyping approach using ProSet-Linda. ProSet is a procedural prototyping language based on the theory of finite sets. The coordination language Linda provides a distributed shared memory model, called tuple space, together with some atomic operations on this shared data space. The combination of both languages, viz. ProSet-Linda, is designed for prototyping parallel algorithms.

The classical control algorithm for symbolic data/model matching in computer vision is the Interpretation Tree search algorithm. Parallel execution can increase the execution performance of model matching, but also make feasible entirely new ways of solving matching problems. In the present paper, we emphasize the development of several parallel algorithms with a prototyping approach. The expected improvements attained by the parallel algorithmic variations for interpretation-tree search are analyzed.

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References

  1. R. Alasdair, A. Bruce, J.G. Mills, and A.G. Smith. CHIMP/MPI User Guide. Technical Report EPCC-KTP-CHIMP-V2-USER 1.2, Edinburgh Parallel Computing Centre, Edinburgh, UK, June 1994.

    Google Scholar 

  2. N. Carriero and D. Gelernter. How to write parallel programs. MIT Press, 1990.

    Google Scholar 

  3. J. Cocke. The search for performance in scientific processors. Communications of the ACM, 31(3):249–253, 1988.

    Article  Google Scholar 

  4. E.-E. Doberkat and D. Fox. Software Prototyping mit SETL. Leitfäden und Monographien der Informatik. Teubner-Verlag, 1989.

    Google Scholar 

  5. E.-E. Doberkat, W. Franke, U. Gutenbeil, W. Hasselbring, U. Lammers, and C. Pahl. ProSet — A Language for Prototyping with Sets. In N. Kanopoulos, ededitor, Proc. Third International Workshop on Rapid System Prototyping, pages 235–248, Research Triangle Park, NC, June 1992.

    Google Scholar 

  6. K.A. Faigin, S.A. Weatherford, J.P. Hoeflinger, D.A. Padua, and P.M. Petersen. The Polaris Internal Representation. International Journal of Parallel Programming, 22(5):553–586, 1994.

    Google Scholar 

  7. R.B. Fisher. Model invocation for three dimensional scene understanding. In J. McDermott, editor, Proc. 10th International Joint Conference on Artificial Intelligence, pages 805–807. Morgan Kaufmann, 1987.

    Google Scholar 

  8. R.B. Fisher. Best-first and ten other variations of the interpretation-tree model matching algorithm. DAI Research Paper No. 717, Dept. of Artificial Intelligence, University of Edinburgh, Edinburgh, UK, September 1994.

    Google Scholar 

  9. R.B. Fisher, A.W. Fitzgibbon, M. Waite, E. Trucco, and M.J.L Orr. Recognition of complex 3-D objects from range data. In S. Impedovo, editor, Proc. 7th International Conference on Image Analysis and Processing, pages 509–606, Monopoli, Bari, Italy, September 1993.

    Google Scholar 

  10. R.B. Fisher, D.K. Naidu, and D. Singhal. Rejection of spurious reflections in structured illumination range finders. In Proc. 2nd Conference on Optical 3D Measurement Techniques, Zurich, Switzerland, October 1993.

    Google Scholar 

  11. Message Passing Interface Forum. MPI: A Message-Passing Interface Standard. Technical Report CS-94-230, University of Tennessee, Computer Science Department, Knoxville, TN, May 1994. (published in the International Journal of Supercomputing Applications, Volume 8, Number 3/4, 1994).

    Google Scholar 

  12. D. Gelernter. Generative communication in Linda. ACM Transactions on Programming Languages and Systems, 7(1):80–112, January 1985.

    Article  Google Scholar 

  13. W.E.L. Grimson. Object Recognition By Computer: The Role of Geometric Constraints. MIT Press, 1990.

    Google Scholar 

  14. W. Hasselbring. CELIP: A cellular language for image processing. Parallel Computing, 14(5):99–109, May 1990.

    Article  Google Scholar 

  15. W. Hasselbring. Prototyping parallel algorithms with ProSet-Linda. In J. Volkert, editor, Parallel Computation (Proc. Second International ACPC Conference), volume 734 of Lecture Notes in Computer Science, pages 135–150, Gmunden, Austria, October 1993. Springer-Verlag.

    Google Scholar 

  16. W. Hasselbring and R.B. Fisher. Investigating parallel interpretation-tree model matching algorithms with ProSet-Linda. DAI Research Paper No. 722, University of Edinburgh, Dept. of Artificial Intelligence, Edinburgh, UK, December 1994. (also available as Software-Technik Memo Nr. 77, University of Dortmund).

    Google Scholar 

  17. M.F. Kaashoek, H.E. Bal, and A.S. Tanenbaum. Experience with the distributed data structure paradigm in Linda. In USENIX/SERC Workshop on Experiences with Building Distributed and Multiprocessor Systems, pages 175–191, Ft. Lauderdale, FL, October 1989.

    Google Scholar 

  18. P. Kruchten, E. Schonberg, and J.T. Schwartz. Software prototyping using the SETL programming language. IEEE Software, 1(4):66–75, October 1984.

    Google Scholar 

  19. E.L. Lawler and D.E. Wood. Branch-and-bound methods: a survey. Operations Research, 14(4):699–719, July 1966.

    Google Scholar 

  20. J.T. Schwartz, R.B.K. Dewar, E. Dubinsky, and E. Schonberg. Programming with Sets — An Introduction to SETL. Springer-Verlag, 1986.

    Google Scholar 

  21. Scientific Computing Associates, New Haven, CT. C-Linda User's Guide & Reference Manual, 1992.

    Google Scholar 

  22. E. Trucco and R.B. Fisher. Computing surface-based representations from range images. In Proc. IEEE International Symposium on Intelligent Control (ISIC-92), pages 275–280, Glasgow, UK, August 1992.

    Google Scholar 

  23. G.V. Wilson. Assessing the usability of parallel programming systems: The Cowichan problems. In Proc. IFIP WG10.3 Working Conference on Programming Environments for Massively Parallel Distributed Systems, Monte Verita, Ascona, Switzerland, April 1994. Birkhäuser Verlag AG.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Dept. of Computer Science, University of Dortmund Informatik 10 (Software Technology), D-44221, Dortmund, Germany

    W. Hasselbring

  2. Dept. of Artificial Intelligence, University of Edinburgh, 5 Forrest Hill, EH1 2QL, Edinburgh, Scotland and UK

    R. B. Fisher

Authors
  1. W. Hasselbring
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  2. R. B. Fisher
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Editor information

Afonso Ferreira José Rolim

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© 1995 Springer-Verlag Berlin Heidelberg

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Hasselbring, W., Fisher, R.B. (1995). Using the ProSet-Linda prototyping language for investigating MIMD algorithms for model matching in 3-D computer vision. In: Ferreira, A., Rolim, J. (eds) Parallel Algorithms for Irregularly Structured Problems. IRREGULAR 1995. Lecture Notes in Computer Science, vol 980. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60321-2_25

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  • DOI: https://doi.org/10.1007/3-540-60321-2_25

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  • Print ISBN: 978-3-540-60321-4

  • Online ISBN: 978-3-540-44915-7

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