Gradient Vector Griding: An Approach to Shape-Based Object Detection in RoboCup Scenarios

  • Hamid Moballegh
  • Naja von Schmude
  • Raúl Rojas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7416)


This paper describes a new method of extraction and clustering of edges in images. The proposed method results a graph of detected edges instead of a binary mask of the edge pixels. The developed algorithm contains a sequential pixel-level scan, and a much smaller second and third pass on the results to determine the connectivities. It is therefore significantly faster than Canny edge detector, performing both edge detection and grouping tasks. The method is developed for a RoboCup scenario, however it can also be applied to any other image as long as the prerequisites are met. The paper explains the idea, discusses the prerequisites and finally presents the implementation results and issues.


  1. 1.
    Bais, A., Sablatnig, R., Novak, G.: Line-based landmark recognition for self-localization of soccer robots. In: Proceedings of the IEEE Symposium on Emerging Technologies, pp. 132–137 (2005)Google Scholar
  2. 2.
    Bandlow, T., Klupsch, M., Hanek, R., Schmitt, T.: Fast Image Segmentation, Object Recognition, and Localization in a RoboCup Scenario. In: Veloso, M.M., Pagello, E., Kitano, H. (eds.) RoboCup 1999. LNCS (LNAI), vol. 1856, pp. 174–185. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  3. 3.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 886–893 (2005)Google Scholar
  4. 4.
    Farag, A.A., Delp, E.J.: Edge linking by sequential search. Pattern Recognition 28(5), 611–633 (1995)CrossRefGoogle Scholar
  5. 5.
    Gunnarsson, K., Wiesel, F., Rojas, R.: The Color and the Shape: Automatic On-Line Color Calibration for Autonomous Robots. In: Bredenfeld, A., Jacoff, A., Noda, I., Takahashi, Y. (eds.) RoboCup 2005. LNCS (LNAI), vol. 4020, pp. 347–358. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Heinemann, P., Sehnke, F., Streichert, F., Zell, A.: Towards a Calibration-Free Robot: The ACT Algorithm for Automatic Online Color Training. In: Lakemeyer, G., Sklar, E., Sorrenti, D.G., Takahashi, T. (eds.) RoboCup 2006. LNCS (LNAI), vol. 4434, pp. 363–370. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  7. 7.
    Mayer, G., Utz, H., Kraetzschmar, G.: Towards autonomous vision self-calibration for soccer robots. In: Proceedings of the 2002 IEEE/RSJ Intl. Conference on Intelligent Robots and Systems (2002)Google Scholar
  8. 8.
    Röfer, T., Jüngel, M.: Fast and Robust Edge-Based Localization in the Sony Four-Legged Robot League. In: Polani, D., Browning, B., Bonarini, A., Yoshida, K. (eds.) RoboCup 2003. LNCS (LNAI), vol. 3020, pp. 262–273. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  9. 9.
    Schulz, H., Liu, W., Stückler, J., Behnke, S.: Utilizing the Structure of Field Lines for Efficient Soccer Robot Localization. In: Ruiz-del Solar, J., Chown, E., Plöger, P. (eds.) RoboCup 2010. LNCS (LNAI), vol. 6556, pp. 397–408. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  10. 10.
    Strasdat, H., Bennewitz, M., Behnke, S.: Multi-cue Localization for Soccer Playing Humanoid Robots. In: Lakemeyer, G., Sklar, E., Sorrenti, D.G., Takahashi, T. (eds.) RoboCup 2006. LNCS (LNAI), vol. 4434, pp. 245–257. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  11. 11.
    Wasik, Z., Saffiotti, R.: Robust color segmentation for the robocup domain. In: Proc. of the Int. Conf. on Pattern Recognition (ICPR), pp. 651–654 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Hamid Moballegh
    • 1
  • Naja von Schmude
    • 1
  • Raúl Rojas
    • 1
  1. 1.Institut für Informatik, Arbeitsgruppe Künstliche IntelligenzFreie Universität BerlinBerlinGermany

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