Advertisement

International Journal of Computer Vision

, Volume 23, Issue 2, pp 169–183 | Cite as

Computing Size Functions from Edge Maps

  • Claudio Uras
  • Alessandro Verri
Article

Abstract

Size functions are integer valued functions of two real variables which have been recently proposed for the representation and recognition of shape. A main limitation of the theory of size functions appeared to be the fragility of the produced representation with respect to edge fragmentation. In this paper it is shown that size functions can actually be defined without making assumptions on the topological structure of the viewed shape. Consequently, size functions can be profitably used even in the presence of fragmented edge maps. In order to demonstrate the potential of size functions for computer vision, a system for shape recognition is described and tested on two different domains. The very good performances of the system indicate that size functions are extremely effective for the analysis of shapes for which geometric models might be difficult to obtain.

Keywords

Image Processing Artificial Intelligence Computer Vision Computer Image Geometric Model 
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. Ayache, N. and Faugeras, O. D. 1986. HYPER: A new approach for the recognition and positioning of two-dimensional objects. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(1):44-54.Google Scholar
  2. Forsyth, D. A., Mundy, J. L., Zisserman, A., Coelho, C., Heller, A., and Rothwell, C. A. 1991. Invariant descriptors for 3D object recognition and pose. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(10):971-991.Google Scholar
  3. Frosini, P. 1990. A distance for similarity classes of submanifolds of a euclidean space. Bull Austral Math Soc., 42:407-416.Google Scholar
  4. Frosini, P. 1991. Measuring shapes by size functions. Proc. of SPIE on Intelligent Robotic Systems, Boston, vol. 1607, pp. 122- 133.Google Scholar
  5. Frosini, P. 1992. Discrete computation of size functions. Journal of Combinatorics and Information System Science, 17(3-4):232- 250.Google Scholar
  6. Grimson, W. E. L. and Lozano-Perez, T. 1984. Model-based recognition and localization from sparse range and tactile data. International Journal of Robotics Research, 3(3):3-35.Google Scholar
  7. Grimson, W. E. L. and Lozano-Perez, T. 1987. Localizing overlapping parts by searching the interpretation tree. IEEE Transactions on Pattern Analysis and Machine Intelligence, 9(4):469- 482.Google Scholar
  8. Huttenlocher, D. P. 1989. Three-dimensional recognition of solid objects from a two-dimensional image. A. I. Memo No. 1045, AI Lab, MIT.Google Scholar
  9. Huttenlocher, D. P., Klanderman, G. A., and Rucklidge, W. J. 1993. Comparing images using the hausdorff distance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(9):850-863.Google Scholar
  10. Lowe, D. G. 1987. Three-dimensional object recognition from single two-dimensional images. Artificial Intelligence, 31(3):355-395.Google Scholar
  11. Teague, M. R. 1980. Image analysis via the general theory of moments. J. Opt. Soc. Am., 70(8):920-930.Google Scholar
  12. Ullman, S. 1987. An approach to object recognition: Aligning pictorial descriptions. A. I. Memo No. 931, AI Lab, MIT.Google Scholar
  13. Uras, C. and Verri, A. 1994a. Studying shape through size functions. In Shape in Picture, Y. O., A. Toet, D. Foster, H. Heijmans, and P. Meer (Eds.), NATO ASI Series F, vol. 126, Springer-Verlag, Berlin Heidelberg, pp. 81-90.Google Scholar
  14. Uras, C. and Verri, A. 1994b. On the recognition of the alphabet of the sign language through size functions. Proc. Intl. Conf. on Pattern Recognition, Jerusalem, vol. II, pp. 334-338.Google Scholar
  15. Verri, A., Uras, C., Frosini, P., and Ferri, M. 1993. On the use of size functions for shape analysis. Biological Cybernetics, 70(2):99- 107.Google Scholar
  16. Verri, A. and Uras, C. 1994. Invariant size functions. In Applications of Invariance in Computer Vision, J. L. Mundy, A. Zisserman, and D. Forsyth (Eds.), Lecture Notes in Computer Science 825, Springer Verlag, Berlin Heidelberg, pp. 215-234.Google Scholar
  17. Verri, A. and Uras, C. 1996. A metric-topological approach to shape representation and recognition (submitted to Image and Vision Computing, 14:189-207).Google Scholar

Copyright information

© Kluwer Academic Publishers 1997

Authors and Affiliations

  • Claudio Uras
    • 1
  • Alessandro Verri
    • 1
  1. 1.Dipartimento di FisicaUniversitá di GenovaGenovaItaly

Personalised recommendations