Advertisement

Uncertainty Analysis of Camera Parameters Computed with a 3D Pattern

  • Carlos Ricolfe-Viala
  • Antonio-José Sánchez-Salmerón
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3617)

Abstract

Camera calibration is a necessary step in 3D modeling in order to extract metric information from images. Computed camera parameters are used in a lot of computer vision applications which involves geometric computation. These applications use camera parameters to estimate the 3D position of a feature in the image. Depending on the accuracy of the computed camera parameter, the precision of the position of the image feature in the 3D scene vary. Moreover if previously the accuracy of camera parameters is known, one technique or another can be choose in order to improve the position of the feature in the 3D scene.

Calibration process consists of a closed form solution followed by a non linear refinement. This non linear refinement gives always the best solution for a given data. For sure this solution is false since input data is corrupted with noise. Then it is more interesting to obtain an interval in which camera parameters are contained more than an accurate solution which is always false.

The aim of this paper is to present a method to compute the interval in which the camera parameter is included. Computation of this interval is based on the residual error of the optimization technique. It is know that calibration process consists of minimize an index. With the residual error of the index minimization an interval can be computed in which camera parameter is. This interval can be used as a measurement of accuracy of the calibration process.

Keywords

camera calibration accuracy evaluation interval estimation 3D pattern 

References

  1. 1.
    Cooperstock, J.R.: Requirements for camera calibration: must accuracy come with high price? In: IEEE Work shop on applications of compueter vision (2004)Google Scholar
  2. 2.
    Faugeras, O.: Three dimensional computer vision: A geometric viewpoint, Cambridge (1993)Google Scholar
  3. 3.
    Golub, G.H., Van Loan, C.F.: Matrix computation, 3rd edn. The Jonh Hopkins University Press (1996)Google Scholar
  4. 4.
    Hartley, R.I.: In defence of the eight point algorithm. IEEE Transactions on pattern analysis and machine intelligence (1997)Google Scholar
  5. 5.
    Hartley, R., Zisserman, A.: Multiple view geometry in computer vision, Cambridge (2000)Google Scholar
  6. 6.
    Kanatani, K.: Statistical Optimization for geometric computation. Springer, Heidelberg (1995)Google Scholar
  7. 7.
    Maybank, S.J., Faugeras, O.D.: A theory of self-calibration of a moving camera. The international Journal of computer vision (1992)Google Scholar
  8. 8.
    Salvi, J., Armangué, X., Battle, J.: A comparative review of camera calibrating methods with accuracy evaluation. Pattern Recognition 35 (2002)Google Scholar
  9. 9.
    Weng, J., Cohen, P., Herniou, M.: Camera calibration with distortion models and accuracy evaluation. IEEE transactions on pattern analysis and machine intelligence (1992)Google Scholar
  10. 10.
    Weng, J., Huang, T.S., Ahuja, N.: Motion and structure from two perspective views: algorithms, error analysis and error estimation. IEEE transactions on pattern analysis and machine intelligence (1989)Google Scholar
  11. 11.
    Zhang, Z.: A flexible new technique for camera calibration. IEEE transactions on pattern analysis and machine intelligence (2000)Google Scholar
  12. 12.
    Zhang, Z.: Calibration with one-dimensional objects. Microsoft technical report (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Carlos Ricolfe-Viala
    • 1
  • Antonio-José Sánchez-Salmerón
    • 1
  1. 1.Department of Systems Engineering and Automatic Control PolytechnicUniversity of ValenciaValenciaSpain

Personalised recommendations