Advertisement

Cellular computers for parallel region-level image processing

  • Azriel Rosenfeld
  • Angela Wu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 153)

Abstract

It is well known that cellular computers can be used very effectively for parallel image processing at the pixel level, by assigning a processor to each level or block of pixels, and passing information as necessary between processors whose blocks are adjacent. This paper discusses the use of cellular computers for parallel processing of images at the region level, assigning a processor to each region and passing information between processors whose regions are related. The basic difference between the pixel and region levels is that the regions (e.g., obtained by segmenting the given image) and relationships differ from image to image, and even for a given image, they do not remain fixed during processing. Thus, one cannot use the standard type of cellular parallelism, in which the set of processors and interprocessor connections remain fixed, for processing at the region level. Reconfigurable cellular computers, in which the set of processors that each processor can communicate with can change during a computation, are more appropriate. A class of such computers is described, and general examples are given illustrating how such a computer could initially configure itself to represent a given decomposition of an image into regions, and dynamically reconfigure itself, in parallel, as regions merge or split.

Keywords

Span Tree Segmented Image Adjacency Graph Outer Border Border Segment 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    S. H. Unger, A computer oriented toward spatial problems, Proc. IRE, 46, 1744–1750 (1958).Google Scholar
  2. 2.
    B. H. McCormick, The Illinois pattern recognition computer — ILLIAC III, IEEE Trans., EC-12, 791–813 (1963).Google Scholar
  3. 3.
    M. J. B. Duff and D. J. Watson, The cellular logic array processor, Computer J., 20, 68–72 (1977).Google Scholar
  4. 4.
    K. E. Batcher, Design of a massively parallel processor, IEEE Trans., C-29, 836–840 (1980).Google Scholar
  5. 5.
    P. Marks, Low level vision using an array processor, Computer Graphics Image Processing, 14, 281–292 (1980).Google Scholar
  6. 6.
    T. Pavlidis, Structural Pattern Recognition, Springer, New York (1977).Google Scholar
  7. 7.
    A. Wu and A. Rosenfeld, Cellular graph automata (I and II), Info. Control, 42, 305–329, 330–353 (1979).Google Scholar
  8. 8.
    A. Wu and A. Rosenfeld, Local reconfiguration of networks of processors, TR-730, Computer Vision Laboratory, Computer Science Center, University of Maryland, College Park, MD (1979).Google Scholar
  9. 9.
    T. Dubitzki, A. Wu and A. Rosenfeld, Local reconfiguration of networks of processors: arrays, trees, and graphs, TR-790, Computer Vision Laboratory, Computer Science Center, University of Maryland, College Park, MD (1979).Google Scholar
  10. 10.
    A. Rosenfeld and A. Wu, Reconfigurable cellular computers, TR-963, Computer Vision Laboratory, Computer Science Center, University of Maryland, College Park, MD (1980). Info. Control, in press.Google Scholar
  11. 11.
    C. J. Rieger, ZMOB: A mob of 256 cooperative Z80A-based microcomputers, Proc. DARPA Image Understanding Workshop, pp. 25–30, November (1979).Google Scholar
  12. 12.
    T. Dubitzki, A. Wu and A. Rosenfeld, Region property computation by active quadtree networks, IEEE Trans., PAMI-3, 626–633 (1981).Google Scholar
  13. 13.
    C. R. Brice and C. L. Fennema, Scene analysis using regions, Artif. Intelligence, 1, 205–226 (1970).Google Scholar
  14. 14.
    L. Kitchen and A. Rosenfeld, Discrete relaxation for matching relational structures, IEEE Trans., SMC-9, 869–874 (1979).Google Scholar
  15. 15.
    L. Kitchen, Relaxation applied to matching quantitative relational structures, IEEE Trans., SMC-10, 96–101 (1980).Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1983

Authors and Affiliations

  • Azriel Rosenfeld
    • 1
  • Angela Wu
    • 1
    • 2
  1. 1.Computer Vision Laboratory, Computer Science CenterUniversity of MarylandCollege ParkUSA
  2. 2.Department of Mathematics, Statistics, and Computer ScienceAmerican UniversityWashington, DCUSA

Personalised recommendations