Towards Parallel Processing of Multisensed Data

  • C. Guerra
  • S. Levialdi
Conference paper
Part of the NATO ASI Series book series (volume 83)


According to applications, data may come from many different sources even simultaneously as in multisensed environments: this implies fast input channels and, consequently, processing elements able to provide the information required to match the specific domain requests. For instance, in an autonomous vehicle control system the telecameras and other sensors should allow the computer unit of the vehicle to decide and manage the driving strategy of such vehicle.


Computer Architecture Medial Axis Systolic Array Edge Pixel Line Detection 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ballard, D.H.: Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition, vol. 3, n. 2, pp. 110–122, 1981.Google Scholar
  2. 2.
    Bongiovanni, G., Guerra C., Levialdi, S.: Computing the Hough transform on a pyramid computer. Submitted, 1987.Google Scholar
  3. 3.
    Chuang, H.Y., Li, C.C.: A systolic array for straight line detection by modified Hough transform. In: IEEE Work, on Comp. Architecture for Pattern Analysis and Image Data Base Management, pp. 300–304, 1985.Google Scholar
  4. 4.
    Cantoni, V., Levialdi, S., edits.: Pyramidal Systems for Computer Vision. Springer-Verlag, Berlin, NATO ARW, Series F, Vol. 25, 1986.MATHGoogle Scholar
  5. 5.
    Chandran, S., Mount, D., Shared memory model and the medial axis transform, In: IEEE Work, on Computer Architectures for Pattern Analysis and Machine Intelligence, pp. 115–121, 1987.Google Scholar
  6. 6.
    Cinque, L., Guerra, C., Levialdi, S.: The medial axis transform on a pyramid computer. In: Proc. of Int. Conf. on Image Analysis and Processing, Positano, Italy, 1989.Google Scholar
  7. 7.
    Cypher, R., Sanz, J.L.C.: The Hough transform has O(n) complexity on a SIMD nxn mesh array architecture. In: IEEE Work, on Computer Architectures for Pattern Analysis and Machine Intelligence, pp. 115–121, 1987.Google Scholar
  8. 8.
    Duda, R.O., Hart, P.E.: Use of the Hough transform to detect lines and curves in pictures. Communie. ACM, vol.15, n. 1, 1972.Google Scholar
  9. 9.
    Fischer, A. L., Highnam, P. T.: Real-time image processing on scanline array processors. In: IEEE Workshop on Computer Architectures for Pattern Analysis and Machine Intelligence, pp. 484–489, 1985.Google Scholar
  10. 10.
    Guerra, C. Hambrusch, S.: Parallel algorithms for line detection on a mesh. Journal of Parallel and Distributed Computing, vol. 6, pp. 1–20, Feb. 1989.CrossRefGoogle Scholar
  11. 11.
    Hough, P. V.: Methods and means to recognize complex patterns, U.S. Patent 3,069,654, 1962.Google Scholar
  12. 12.
    Kung, H.T., Webb, J.: Mapping image processing operations onto a linear systolic machine. Distributed Computing, pp. 246–257, 1986.Google Scholar
  13. 13.
    Ibrahim, H. A., Kender, J.R., Shaw, D.E.: The analysis and performance of two middle-level vision tasks on a fine-grained SIMD tree machine. In: Proc. of the IEEE. Conf. on Computer Vision and Pattern Recognition, pp. 248–256, 1985.Google Scholar
  14. 14.
    Hillis, W. D.: the Conncetion Machine. MIT Press, Mass, 1985.Google Scholar
  15. 15.
    Jolion, J. M., Rosenfeld, A.: An O(logn) pyramid Hough transform. Pattern Recognition Letters, vol. 9, pp. 343–349, 1989.CrossRefMATHGoogle Scholar
  16. 16.
    Levialdi, S., edit.: Multicomputer Vision. Academic Press, London, 1985.Google Scholar
  17. 17.
    Li, H., Lavin, M., Le Master, R.: Fast Hough transform: a hierarchical approach. Computer Vision Graphics and Image Processing, vol 36, pp. 139–161, 1986.CrossRefGoogle Scholar
  18. 18.
    Li, H., Maresca, M.: Polymorphic torus: a new architecture for vision computation. In: Proc. of the Work, on Computer Architecture for Pattern Anal, and Mach. Intel., pp.176–184, 1987.Google Scholar
  19. 19.
    Little, J.J., Blelloch, G. and Cass, T.: How to program the Connection Machine. In: Proc. of the Work, on Computer Architecture for Pattern Anal, and Mach. Intel., pp. 11–19, 1987.Google Scholar
  20. 20.
    Miller, R., Stout, Q.: Convexity algorithms for pyramid computers. In: Proc. Int. Conf. Parallel Processing, pp. 177–184, 1984.Google Scholar
  21. 21.
    Miller, R., Stout, Q.: Data movement techniques for the pyramid computer. SIAM J. on Computing, vol. 16, n.1, pp. 38–60, 1987.CrossRefMATHMathSciNetGoogle Scholar
  22. 22.
    Mudge, T. N.: Vision algorithms for hypercube machines. In: Proc. Comp. Arch, for Pattern Analysis and Image Database Manag., pp. 225–231, 1985.Google Scholar
  23. 23.
    Rosenfeld, A., (Ed.): Multiresolution Image Processing and Analysis. Springer-Verlag, 1984.MATHGoogle Scholar
  24. 24.
    Sanz, J.L.C., Dinstein, I.: Projection-based geometrical feature extraction for computer vision: algorithms in pipeline architectures. IEEE Trans, on PAMI, vol.9, n. 1, pp. 160–168, 1987.CrossRefGoogle Scholar
  25. 25.
    Stout, Q.: Hypercubes and pyramids. In: Pyramidal systems for Computer Vision, NATO ASI series, V. Cantoni and S. Levialdi eds., pp. 75–89, 1986.Google Scholar
  26. 26.
    Tanimoto, S.L.: Programming techniques for hierarchical parallel image processors. In: Multicomputers for image processing, (K. Preston, and L. Uhr eds.), Academic Press, pp. 421–429, 1984.Google Scholar
  27. 27.
    Tanimoto, S. L., Kingler, A.: Structured Computer Vision. Academic Press., 1980.Google Scholar
  28. 28.
    Uhr, L.: Layered recognition cone networks that preprocess, classify, and describe. IEEE Trans, on Comp., pp. 758–768, 1972.Google Scholar
  29. 29.
    Valiant, L. G.: A scheme for parallel communication. SIAM J. on Computing, vol. 11, pp. 350–361, 1982.CrossRefMATHMathSciNetGoogle Scholar
  30. 30.
    Wu, A., Bhaskar, S.K., Rosenfeld, AA.: Computation of geometric properties from the medial axis transform in O(nlogn) time. CVIP, vol. 34, pp. 76–92, 1986.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1992

Authors and Affiliations

  • C. Guerra
    • 1
  • S. Levialdi
    • 1
  1. 1.Dipartimento MatematicaUniversity of RomeRomaItaly

Personalised recommendations