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Robust Artificial Landmark Recognition Using Polar Histograms

  • Pablo Suau
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3808)

Abstract

New results on our artificial landmark recognition approach are presented, as well as new experiments in order to demonstrate the robustness of our method. The objective of our work is the localization and recognition of artificial landmarks to help in the navigation of a mobile robot. Recognition is based on interpretation of histograms obtained from polar coordinates of the landmark symbol. Experiments prove that our approach is fast and robust even if the database has an high number of landmarks to compare with.

Keywords

Mobile Robot Robot Navigation Landmark Localization Polar Image Landmark 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.

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References

  1. [1]
    Todt, E., Torras, C.: Detection of Natural Landmarks Through Multiscale Opponent Features. In: 15th International Conference on Pattern Recognition (ICPR 2000), Barcelona, Spain, vol. 3, pp. 3988–3991 (2000)Google Scholar
  2. [2]
    Hsien, J.C., Chen, S.Y.: Road Sign Detection and Recognition Using Markov Model. In: 14th Workshop on Object-Orient Technology and Applications (OOTA 2003), Taiwan, pp. 529–536 (2003)Google Scholar
  3. [3]
    Piccioli, G., De Micheli, E., Parodi, P., Campani, M.: A Robust method for road sign detection and recognition. Image and Vision Computing 14, 209–223 (1996)CrossRefGoogle Scholar
  4. [4]
    Zadeh, M.M., Kasvand, T., Suen, C.Y.: Localization and Recognition of Traffic Signs for Automated Vehicle Control Systems. In: Conf. on Intelligent Transportation Systems, part of SPIE’s Intelligent Systems and Automated Manufacturing, Pittsburgh, USA, pp. 272–282 (1997)Google Scholar
  5. [5]
    Bernier, T., Landry, J.A.: A New Method for Representationg and Matching Shapes of Natural Objects. Pattern Recognition 36(8), 1711–1723 (2003)CrossRefGoogle Scholar
  6. [6]
    Fekete, S.P.: Simplicity and Hardness of the Maximum Traveling Salesman Problem under Geometric Distances. In: Proc. Tenth ACM-SIAM Symposium on Discard Algorithms (SODA 1999), Maryland, USA, pp. 337–345 (1999)Google Scholar
  7. [7]
    Cha, S.H., Srihari, S.N.: Distance Between Histograms of Angular Measurements and its Application to Handwritten Character Similarity. In: 15th International Conference on Pattern Recognition (ICPR 2000), Barcelona, Spain, pp. 21–24 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Pablo Suau
    • 1
  1. 1.Departamento de Ciencia de la Computación e Inteligencia ArtificialUniversidad de AlicanteAlicanteSpain

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