Self-calibration from Planes Using Differential Evolution

  • Luis Gerardo de la Fraga
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5856)

Abstract

In this work the direct self-calibration of a camera from three views of a unknown planar structure is proposed. Three views of a plane are sufficient to determine the plane structure, the view’s positions and orientations and the camera’s focal length. This is a non-linear optimization problem that is solved using the heuristic Differential Evolution. Once an initial structure is obtained, the bundle adjustment can be used to incorporate more views and estimate other camera intrinsic parameters and possible lens distortion. This new self-calibration method is tested with real data.

Keywords

Computer Vision camera self-calibration self-calibration from planes differential evolution 

References

  1. 1.
    Hartley, R., Zisserman, A.: Multiple view geometry in computer vision, 2nd edn. Cambridge Uni. Press, Cambridge (2003)Google Scholar
  2. 2.
    Heyden, A., Astrom, K.: Flexible calibration: minimal cases for auto-calibration. In: ICCV-1999, pp. 350–355. IEEE Press, Los Alamitos (1999)Google Scholar
  3. 3.
    Hemayed, E.E.: A survey of camera self-calibration. In: AVSS 2003: Proc. IEEE Conf. on Advanced and Signal Based Surveillance. IEEE Press, Los Alamitos (2003)Google Scholar
  4. 4.
    Zhang, Z.: A flexible new technique for camera calibration. IEEE Trans. on Patt. Anal. & Mach. Intel. 22, 1330–1334 (2000)CrossRefGoogle Scholar
  5. 5.
    Sturm, P.F., Maybank, S.J.: On plane-based camera calibration: A general algorithm, singularities, applications. In: CVPR, pp. 432–437 (1999)Google Scholar
  6. 6.
    Menudet, J.F., Becker, J.M., Fournel, T., Mennessier, C.: Plane-based camera self-calibration by metric rectification of images. Image and Vision Computing 26, 913–934 (2008)CrossRefGoogle Scholar
  7. 7.
    Bocquillon, B., Gurdjos, P., Crouzil, A.: Towards a guaranteed solution to plane-based self-calibration. In: Narayanan, P.J., Nayar, S.K., Shum, H.-Y. (eds.) ACCV 2006. LNCS, vol. 3851, pp. 11–20. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  8. 8.
    Storn, R., Price, K.V.: Differential evolution – a simple and efficient heuristic for global optimization over continuos spaces. J. of Global Optimization 11(4), 341–359 (1997)MATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Herman, G.T.: Image Reconstruction from Projections: The Fundamentals of Computerized Tomography. Academic Press, London (1980)MATHGoogle Scholar
  10. 10.
    Mezura, E., Velázquez, J., Coello, C.A.: A comparative study of differential evolution variants for global optimization. In: GECCO 2006, New York, NY, USA, pp. 485–492. ACM Press, New York (2006)CrossRefGoogle Scholar
  11. 11.
    Chakraborty, U.K.: Advances in Differential Evolution. Studies in Computational Intelligence. Springer, Heidelberg (2008)Google Scholar
  12. 12.
    Zielinski, K., Laur, R.: Stopping criteria for differential evolution in constrained single-objective optimization. In: Advances in Differential Evolution. Springer, Heidelberg (2008)Google Scholar
  13. 13.
    Hansen, N.: Comparisons results among the accepted papers to the special session on real-parameter optimization at CEC 2005 (2006), http://www.ntu.edu.sg/home/epnsugan/index_files/CEC-05/compareresults.pdf
  14. 14.
    Auger, A., Hansen, N.: A restart CMA evolution strategy with increasing population size. In: Proceedings of the Congress on Evolutionary Computation CEC-2005, pp. 1769–1776 (2005)Google Scholar
  15. 15.

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Luis Gerardo de la Fraga
    • 1
  1. 1.Computer Science DepartmentCinvestavMexico CityMexico

Personalised recommendations