Advertisement

A Fast Image Analysis Technique for the Line Tracking Robots

  • Krzysztof Okarma
  • Piotr Lech
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6114)

Abstract

Fast and simplified image processing and analysis methods can be successfully implemented for the robot control algorithms. Statistical methods seem to be very useful for such an approach, mainly because a significant reduction of analysed data is possible. In the paper the use of the fast image analysis based on the Monte Carlo area estimation for the simplified binary representation of the image is analysed and proposed for the mobile robot control. A possible implementation of the proposed method can applied in the line tracking robots and such application has been treated as the basic one for the testing purposes.

Keywords

robot vision statistical image analysis line tracking robots 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Chen, D., Odobez, J.-M.: Sequential Monte Carlo Video Text Segmentation. In: International Conference on Image Processing, ICIP 2003, vol. 3, pp. 21–24. IEEE Press, New York (2003)Google Scholar
  2. 2.
    Dupuis, J., Parizeau, M.: Evolving a Vision-Based Line-Following Robot Controller. In: 3rd Canadian Conference on Computer and Robot Vision, June 7-9, pp. 75–75 (2006), doi:10.1109/CRV.2006.3Google Scholar
  3. 3.
    Fearnhead, P.: Computational Methods for Complex Stochastic Systems: A Review of Some Alternatives to MCMC. Statistics and Computing 18(2), 151–171 (2008)CrossRefMathSciNetGoogle Scholar
  4. 4.
    Okarma, K., Lech, P.: Monte Carlo Based Algorithm for Fast Preliminary Video Analysis. In: Bubak, M., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2008, Part I. LNCS, vol. 5101, pp. 790–799. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  5. 5.
    Okarma, K., Lech, P.: A Statistical Reduced-Reference Approach to Digital Image Quality Assessment. In: Bolc, L., Kulikowski, J.L., Wojciechowski, K. (eds.) ICCVG 2008. LNCS, vol. 5337, pp. 43–54. Springer, Heidelberg (2009)Google Scholar
  6. 6.
    Rahman, M., Rahman, M.H.R., Haque, A.L., Islam, M.T.: Architecture of the Vision System of a Line Following Mobile Robot Operating in Static Environment. In: 9th International Multitopic Conference, IEEE INMIC 2005, December 24-25, pp. 1–8 (2005), doi:10.1109/INMIC.2005.334473Google Scholar
  7. 7.
    Rubinstein, R.Y.: Simulation and the Monte Carlo Method. Wiley, Chichester (1981)zbMATHCrossRefGoogle Scholar
  8. 8.
    Vermaak, J., Ikoma, N., Godsill, S.J.: Sequential Monte Carlo Framework for Extended Object Tracking. IEE Proc. Radar Sonar Navig. 152(5), 353–363 (2005)CrossRefGoogle Scholar
  9. 9.
    Zhai, Y., Shah, M.: Video Scene Segmentation Using Markov Chain Monte Carlo. IEEE Trans. on Multimedia 8(4), 686–697 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Krzysztof Okarma
    • 1
  • Piotr Lech
    • 1
  1. 1.Szczecin, Faculty of Electrical Engineering, Chair of Signal Processing and Multimedia EngineeringWest Pomeranian University of TechnologySzczecinPoland

Personalised recommendations