A New Video Rate Region Color Segmentation and Classification for Sony Legged RoboCup Application

  • Aymeric de Cabrol
  • Patrick Bonnin
  • Thomas Costis
  • Vincent Hugel
  • Pierre Blazevic
  • Kamel Bouchefra
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4020)


Whereas numerous methods are used for vision systems embedded on robots, only a few use colored region segmentation mainly because of the processing time. In this paper, we propose a real-time (i.e. video rate) color region segmentation followed by a robust color classification and region merging dedicated to various applications such as RoboCup four-legged league or an industrial conveyor wheeled robot. Performances of this algorithm and confrontation with other existing methods are provided.


Edge Point Video Rate Region Segmentation Quadruped Robot Blob Detection 
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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  1. 1.
    Silly-Chetto, M., Garcia, T., Grellier, F.: Open source components for embedded real time applications. In: IEEE Int. Conf. on Control and Automation, Xiamen, China (June 2002)Google Scholar
  2. 2.
    Olave, A., Wang, D., Wong, J., Tam, T., Leung, B., Kim, M., Brooks, J., Chang, A., Von Huben, N., Sammut, C., Hengst, B.: The UNSW RoboCup 2002 Legged League Team. In: 6th Int. Workshop on RoboCup 2002, Fukuoka, Japan (2002)Google Scholar
  3. 3.
    Bruce, J., Balch, T., Veloso, M.: Fast and inexpensive color image segmentation for interactive robots. In: Proceedings of the 2000 IEEE / RSJ Int. Conf. on Intelligent Robots and Systems (IROS 2000), vol. 3, pp. 2061–2066 (2000)Google Scholar
  4. 4.
    Zagal, J.C., Ruiz-del-Solar, J., Guerrero, P., Palma, R.: Evolving Visual Object Recognition for Legged Robots. In: Polani, D., Browning, B., Bonarini, A., Yoshida, K. (eds.) RoboCup 2003. LNCS, vol. 3020, pp. 181–191. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  5. 5.
    Hugel, V., Bonnin, P., Bouramoué, J.C., Solheid, D., Blazevic, P., Duhaut, D.: Quadruped Robot Guided by Enhanced Vision System and Supervision Modules. In: Asada, M., Kitano, H. (eds.) RoboCup 1998. LNCS, vol. 1604, pp. 485–490. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  6. 6.
    Cameron, D., Barnes, N.: Knowledge-based autonomous dynamic colour calibration. In: Polani, D., Browning, B., Bonarini, A., Yoshida, K. (eds.) RoboCup 2003. LNCS, vol. 3020, pp. 226–237. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  7. 7.
    Jüngel, M., Hoffmann, J., Lötzsch, M.: A Real-Time Auto-Adjusting Vision System for Robotic Soccer. In: Polani, D., Browning, B., Bonarini, A., Yoshida, K. (eds.) RoboCup 2003. LNCS, vol. 3020, pp. 214–225. Springer, Heidelberg (2004)CrossRefGoogle 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, vol. 3020, pp. 262–273. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  9. 9.
    von Hundelshausen, F., Rojas, R.: Tracking regions. In: Polani, D., Browning, B., Bonarini, A., Yoshida, K. (eds.) RoboCup 2003. LNCS, vol. 3020, pp. 250–261. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  10. 10.
    DeSouza, G.N., Kak, A.C.: Vision for Mobile Robot Navigation: A Survey. IEEE Trans. on Pattern Analysis and Machine Intelligence PAMI 24(2) (February 2002)Google Scholar
  11. 11.
    Proceedings of IROS 2001, IEEE Int. Conf. on Intelligent Robots and Systems, Hawai, USA (October 2001)Google Scholar
  12. 12.
    Proceedings of ROMAN 2001, IEEE Int Workshop on Robot and Human Communication, Bordeaux - Paris FRANCE (September 2001)Google Scholar
  13. 13.
    Priese, L., Rehrmann, V.: A Fast Hybrid Color Segmentation Method. In: Proceedings Mustererkennung, DAGM Symposium 1993, pp. 297–304, Lübeck (1993)Google Scholar
  14. 14.
    Priese, L., Rehrmann, V., Schian, R., Lakmann, R.: Traffic Sign Recognition Based on Color Image Evaluation. In: Proceedings IEEE Intelligent Vehicles Symposium 1993, Tokyo, Japan, pp. 95–100 (July 2003)Google Scholar
  15. 15.
    Horowitz, S., Pavlidis, T.: Picture segmentation by a direct split-and-merge procedure. In: Sec. Int. Joint Conf. on Pattern Recognition, pp. 424–433 (1974)Google Scholar
  16. 16.
    Bonnin, P., Blanc Talon, J., Hayot, J., Zavidovique, B.: A new edge point / region cooperative segmentation deduced from a 3D scene reconstruction application. In: SPIE 33rd Ann. Int. Symp. on Optical & Optoelectronic Applied Science & Engineering, San-Diego, California. Application of digital image processing, vol. XII (August 1989)Google Scholar
  17. 17.
    Rosenfeld, A., Pfalz, J.L.: Sequential operations in digital picture processing. Journal of ACM 13(4) (1966)Google Scholar
  18. 18.
    Kirsh: Computer determination of the constituent structure of biological images. Computer & biomedical research, USA 4(3), 315–328 (1971)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Aymeric de Cabrol
    • 1
  • Patrick Bonnin
    • 1
    • 2
  • Thomas Costis
    • 2
  • Vincent Hugel
    • 2
  • Pierre Blazevic
    • 2
  • Kamel Bouchefra
    • 2
  1. 1.Laboratoire de Transport et de Traitement de l’Information L2TI, Institut GaliléeVilletaneuseFrance
  2. 2.Laboratoire de Mécatronique et Robotique de VersaillesVélizyFrance

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