Skip to main content
SpringerLink
Log in

Multidimensional shape description and recognition using mathematical morphology

  • Published:
Journal of Intelligent and Robotic Systems Aims and scope Submit manuscript

Abstract

In this paper, a tool for describing the geometrical structure of a continuous or discrete, multidimensional signal is considered. The origins and foundations of this tool, which we call the pecstrum, lie in the principles of mathematical morphology. The pecstrum is defined, its properties are studied, its computation is investigated, and some examples are given. The suitability of the pecstrum as a shape descriptor is examined. Finally, a practical system for multidimensional shape recognition that uses the pecstrum is introduced, tested, and evaluated.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. MatheronG., Random Sets and Integral Geometry, Wiley, New York, (1975).

    Google Scholar 

  2. SerraJ., Image Analysis and Mathematical Morphology, Academic Press, New York (1982).

    Google Scholar 

  3. Maragos, P. A., A unified theory of translation-invariant systems with applications to morphological analysis and coding of images, PhD thesis, Georgia Institute of Technology (1985).

  4. Sternberg, S. R., Biomedical image processing, IEEE Computer, January 1983, pp. 22–34.

  5. SternbergS. R., Cellular computers and biomedical image processing, in Lecture Notes in Medical Informatics (ed. J.Solansky and J. C.Bisconte), Vol. 17, Springer-Verlag, Berlin, pp. 294–319 (1980).

    Google Scholar 

  6. Bronskill, J. F., Multidimensional shape description and recognition using mathematical morphology, MASc thesis, University of Toronto (1986).

  7. LantuejoulC., An image analyser, in Languages and Architectures for Image Processing, (eds. M. J. B.Duff and S.Levialdi), Academic Press, London (1981).

    Google Scholar 

  8. Lougheed, R. M., A high speed recirculating neighborhood processing architecture, Architectures and Algorithms for Digital Image Processing II, (ed. F. J. Corbett), Proc. SPIE 534, pp. 22–33 (1985).

  9. Sternberg, S. R., Parallel architectures for image processing, Proc. 3rd IEEE Intern. Computer Software and Applications Conf. (COMPSAC), Chicago, pp. 712–717 (1979).

  10. Sternberg, S. R., Language and architecture for parallel image processing, in Pattern Recognition in Practice, (ed. E. S. Gelsema and L. N. Kanal), North-Holland, pp. 35–44 (1980).

  11. Lougheed, R. M., McCubbrey, D. L., and Sternberg, S. R., Cytocomputers: architectures for parallel image processing, Proceedings of the Workshop on Picture Data Description and Management, Asilomar, California, pp. 281–286 (1980).

  12. SternbergS. R., Parallel architectures for image processing, in Real-Time/Parallel Computing Image Analysis (eds. M.Onoe, K.PrestonJr., A.Rosenfeld), Plenum Press, New York, pp. 347–359 (1981).

    Google Scholar 

  13. Sternberg, S. R. and Sternberg, E. S., Industrial inspection by morphological virtual gauging, Proc. of 1983 IEEE Workshop on Comput. Architec. for Pattern Analysis and Image Database Management, Pasadena, Calif., pp. 237–247 (1983).

  14. Sternberg, S. R., Machine vision non-contact gauging, SME Applied Machine Vision Conference, Technical Paper MS84-150, (1984).

  15. LevineM. D., Vision in Man and Machine, McGraw-Hill, New York (1985).

    Google Scholar 

  16. PavlidisT., A review of algorithms for shape analysis, Computer Graphics and Image Processing 7, 243–258 (1978).

    Google Scholar 

  17. SproullR. F., SutherlandW. R., and UllnerM. K., Device-Independent Graphics, McGraw-Hill, New York (1985).

    Google Scholar 

  18. Ahuja, N., Bridwell, N., Nash, C., and Huang, T. S., Three dimensional robot vision, 1982 IEEE Workshop on Industrial Applications of Machine Vision, Research Triangle Park, N.C., pp. 206–213 (1982).

  19. AnderssonR. L., Real-time gray-scale video processing using a moment generating chip, IEEE J. Robotics and Automation RA-1, No. 2, 79–85 (1985).

    Google Scholar 

  20. Biswas, S. N. and Chaudhuri, B. B., On the generation of discrete circular objects and their properties, Computer Vision, Graphics, and Image Processing, No. 32, 158–170 (1985).

  21. Serra, J., Digital morphology in the 3-D space, Proc. 1982 IEEE Intern. Conf. on Acoust., Speech, and Signal Processing (ICASSP), Paris, France, pp. 843–845 (1982).

  22. Ally, A., Object recognition based on mathematical morphology, Bachelor's Thesis, Department of Electrical Engineering, University of Toronto (1986).

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, University of Toronto, M5S 1A4, Toronto, Ontario, Canada

    J. F. Bronskill & A. N. Venetsanopoulos

Authors
  1. J. F. Bronskill
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. N. Venetsanopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Bronskill, J.F., Venetsanopoulos, A.N. Multidimensional shape description and recognition using mathematical morphology. Journal of Intelligent and Robotic Systems 1, 117–143 (1988). https://doi.org/10.1007/BF00348719

Download citation

  • Received:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF00348719

Key words

Search

Navigation