Advertisement

MARIS: Map Recognition Input System

  • Satoshi Suzuki
  • Toyomichi Yamada
Part of the NATO ASI Series book series (volume 65)

Abstract

A map recognition input system called MARIS is developed to digitize large-scale maps into a layered data form. This paper presents an experimental workstation, a vector-based recognition method, and an intelligent interaction function which are proposed in order to enhance input speed. The recognition method is capable of extracting building lines, contour lines, and lines representing railways, roads and water areas. Experimental results show that input time using MARIS can be reduced to about 25% of that of a system using a conventional interactive digitizer.

Keywords

Feature Point Graphic Processing Unit Contour Line Vector Data Automatic Recognition 
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.
    Misawa, H. et al.: Well-informed geographer: An integrated geographic information system. NEC Technical Journal. 37, 32–40 (1984)Google Scholar
  2. 2.
    Samet, H., Rosenfeld, A., Shaffer, C.A. and Webber, R.E.: A geographic information system using quadtrees. Pattern Recognition. 17, 647–656 (1984)CrossRefGoogle Scholar
  3. 3.
    Agui, T., Litsuka, H. and Nakajima, M.: Image processing for urban map utilizing pyramid hierarchical data. Trans. Inst. Electron. Commun. Eng. Japan. J65-D, 1243–1249 (1982)Google Scholar
  4. 4.
    Suzuki, S., Kosugi, M. and Hoshino, T.: Automatic line drawing recognition of large-scale maps. Optical Engineering. 26, 642–649 (1987)Google Scholar
  5. 5.
    Ejiri, M. et al.: Automatic recognition of designs and maps. Proc. 7th Int. Conf. on Pattern Recognition 1984, Montreal, pp. 1296–1305. Washington, D.C.: IEEE Computer Society Press 1984Google Scholar
  6. 6.
    Tavakoli, M. and Rosenfeld, A.: Building and road extraction from aerial photographs. IEEE Trans. Syst. Man Cybernet. SMC-12, 84–91 (1982)CrossRefGoogle Scholar
  7. 7.
    Nagao, M., Matsuyama, T. and Ikeda, Y.: Region extraction and shape analysis in aerial photographs. Comput. Graphics Image Process. 10, 195–223 (1979)CrossRefGoogle Scholar
  8. 8.
    Huertas, A. and Nevatia, R.: Detecting buildings in aerial images, Comput. Vision, Graphics Image Process. 41, 131–152 (1988)CrossRefGoogle Scholar
  9. 9.
    Fua, P. and Hanson, A.J.: Using generic geometric models for intelligent shape extraction, Proc. AAAI 1987, St. Paul, pp. 706–711. Palo Alto: Morgen Kaufmann Publishers 1987Google Scholar
  10. 10.
    Nakajima, M., Agui, T. and Iituka, H.: A graphical structure extracting method from an urban map using parallel vector traces. Trans. Inst. Electron. Commun. Eng. Japan. J67-D, 1419–1426 (1984)Google Scholar
  11. 11.
    Nevatia, R. and Babu, K.R.: Linear feature extraction and description. Comput. Graphics Image Process. 13, 257–269 (1980)CrossRefGoogle Scholar
  12. 12.
    McKeown, D.M. Jr. and Denlinger, J.L.: Cooperative methods for road tracking in aerial imagery, Proc. IEEE Conf. CVPR’88, Annbor, pp. 662–672. Washington, D.C.: IEEE Computer Society Press 1988Google Scholar
  13. 13.
    Amin, T.J. and Kasturi, R.: Map data processing: recognition of lines and symbols. Optical Engineering. 26, 354–358 (1987)Google Scholar
  14. 14.
    Kameyama, W., Oka, H. and Tominaga, H.: Separation and storage method for the elements of map image and the experimental system. Trans. Inst. Electron. Inf. Commun Eng. Japan. J70-D, 1941–1952 (1987)Google Scholar
  15. 15.
    Agui, T. and Furukawa, T.: A method for extracting and restoring of contour lines on a contour map. Trans. Inst. Electron. Commun. Eng. Japan. E63, 581–587 (1980)Google Scholar
  16. 16.
    Ougaki, T., Minoh, M. and Sakai, T.: Automatic character extraction for urban maps. Proc. the 29th IPSJ Annual Convention, 6M-7. Tokyo:Information Processing Society of Japan 1984Google Scholar
  17. 17.
    Harris, J.F., Kittler, J., Llewellyn, B. and Preston, G.: A modular system for interpreting binary pixel representations of line-structured data. Pattern Recognition Theory and Applications. NATO Advanced Study Institutes Series C, Vol.81, pp.311–351. 1982Google Scholar
  18. 18.
    Musavi, M.T., Shirvaikar, M.V., Ramanathan, E. and Nekovei, A.R.: A vision based method to automate map processing. Pattern Recognition. 21, 319–326 (1988)CrossRefGoogle Scholar
  19. 19.
    Suzuki, S. and Yamada, T.: MARIS: Map recognizing input system. Proc. Int. Workshop on Industrial Applications of Machine Vision & Machine Intell. 1987, Tokyo, pp.214–219. New York: IEEE Publishing Services 1987Google Scholar
  20. 20.
    Pamphlet of LASERTRK. England: Laser-Scan Labs. LimitedGoogle Scholar
  21. 21.
    Lewis, P.H. and Goodson, K.J.: Knowledge based interpretation of line drawing images. Proc. IAPR Workshop on Computer Vision 1988, Tokyo, pp. 216–219.Google Scholar
  22. 22.
    Yamahira, T., Koyama, Y. and Kasahara, Y.: Hybrid system for both figure and image data. Information Processing Society of Japan, Technical Report CV42–26, 1986.Google Scholar
  23. 23.
    Arakawa, H., Tokuda, N., Suzuki, K. and Yamada, S.: Distributed processing equipment for multi-media data processing use. Electrical Communications Laboratories Technical Journal. 35, 311–320 (1986).Google Scholar
  24. 24.
    Kurokawa, H., Matsumoto, K. Temma, T., Iwashita, M. and Nukiyama, T.: The ar-chitecture and performance of image pipeline processor. Proc. Int. Conf. VLSI’83, pp. 275–284.Google Scholar
  25. 25.
    Rosenfeld, A. and Kak, A.C.: Digital Picture Processing. Vol. 2. 2nd ed. New York: Academic Press 1982.Google Scholar
  26. 26.
    Deutsch, E.S.: Thinning algorithms on rectangular, hexagonal, triangular arrays. Comm. Ass. Comput. Mach. 15, 827–837 (1972)Google Scholar
  27. 27.
    Suzuki, S.: Graph-based vectorization method for line patterns. Proc. IEEE Conf. CVPR’88, Annbor, pp. 616–621. Washington, D.C.: IEEE Computer Society Press 1988Google Scholar
  28. 28.
    Suzuki, S.: Transformation of binary images into perfect line patterns. 1989 Spring National Convention Record, D-250. Tokyo: Inst. Electron. Information Commun Eng. Japan 1989Google Scholar
  29. 29.
    Suzuki, S. and Yamada, T.: MARIS: Map recognition input system. Pattern Recognition. (Submitted)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1990

Authors and Affiliations

  • Satoshi Suzuki
    • 1
  • Toyomichi Yamada
    • 1
  1. 1.NTT Human Interface LaboratoriesYokosuka 238-03Japan

Personalised recommendations