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Extraction and Removal of Layers from Map Imagery Data

  • Alexey Podlasov
  • Eugene Ageenko
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3540)

Abstract

Map images are composed of semantic layers depicted in arbitrary color. Layer extraction and removal is often needed for improving readability as well as for further processing. When image is separated into the set of layers with respect to the colors, it results in appearance of severe artifacts because of the layer overlapping. In this way the extracted layers differ from the semantic data, which affects further map image processing analysis tasks. In this work, we introduce techniques for extraction and removal of the semantic layers from the map images. The techniques utilize low-complexity morphological image restoration algorithms. The restoration provides good quality of the reconstructed layers, and alleviates the affect of artifacts on the precision of image analysis tasks.

Keywords

Mathematical Morphology Semantic Data Color Layer Field Layer Layer Removal 
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.

References

  1. 1.
    Fox, E.A., et al. (ed.): Digital Libraries. [Special issue of] Communications of the ACM 38(4) (1995)Google Scholar
  2. 2.
    NLS: National Land Survey of Finland, Opastinsilta 12 C, P.O.Box 84, 00521 Helsinki, Finland, http://www.nls.fi/index_e.html
  3. 3.
    Fränti, P., Ageenko, E., Kopylov, P., Gröhn, S., Berger, F.: Compression of map images for real-time applications. Image and Vision Computing 22(13), 1105–1115 (2004)CrossRefGoogle Scholar
  4. 4.
    Pitas, I., Venetsanopoulos, A.N.: Nonlinear digital filters: principles and applications. Kluwer, Boston (1990)zbMATHGoogle Scholar
  5. 5.
    Dougherty, E.R., Astola, J. (eds.): Nonlinear Filters for Image Processing. SPIE Optical Engineering Press, San Jose (1997)Google Scholar
  6. 6.
    Dougherty, E.R.: ‘Optimal mean-square n-observation digital morphological filters. Part I: Optimal binary filters. Computer Vision, Graphics, and Image Processing 55, 36–54 (1992)zbMATHGoogle Scholar
  7. 7.
    Wah, F.M.: A binary image preprocessor for document quality improvement and data reduction. In: Proc. Int. Conf. on Acoustic, Speech, and Signal Processing-ICASSP 1986, pp. 2459–2462 (1986)Google Scholar
  8. 8.
    Ping, Z., Lihui, C., Alex, K.C.: Text document filters using morphological and geometrical features of characters. In: Proc. Int. Conf on Signal Processing-ICSP 2000, pp. 472–475 (2000)Google Scholar
  9. 9.
    Randolph, T.R., Smith, M.J.T.: Enhancement of fax documents using a binary angular representation. In: Proc. Int. Symp. on Intelligent Multimedia, Video and Signal Processing, Hong Kong China, May 2-4, pp. 125–128 (2001)Google Scholar
  10. 10.
    Zheng, Q., Kanungo, T.: Morphological degradation models and their use in document image restoration., University of Maryland, USA, Technical Report, LAMP-TR-065 CAR-TR-962 N660010028910/IIS9987944 (2001)Google Scholar
  11. 11.
    Ageenko, E., Fränti, P.: Context-based filtering of document images. Pattern Recognition Letters 21(6-7), 483–491 (2000)CrossRefGoogle Scholar
  12. 12.
    Kolesnikov, A., Fränti, P.: Data reduction of large vector graphics. Pattern Recognition 38(3), 381–394 (2005)zbMATHCrossRefGoogle Scholar
  13. 13.
    Heijmans, H.J.A.M.: Morphological image operators. Academic Press, Boston (1994)zbMATHGoogle Scholar
  14. 14.
    Matheron, G.: Random Sets and Integral Geometry. J. Wiley & Sons, New York (1975)zbMATHGoogle Scholar
  15. 15.
    Serra, J.: Image Analysis and Mathematical morphology. Academic Press, London (1982)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Alexey Podlasov
    • 1
  • Eugene Ageenko
    • 1
  1. 1.Department of Computer ScienceUniversity of JoensuuJoensuuFinland

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