Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Line Geometry and Camera Autocalibration

  • Published:
Journal of Mathematical Imaging and Vision Aims and scope Submit manuscript

Abstract

We provide a completely new rigorous matrix formulation of the absolute quadratic complex (AQC), given by the set of lines intersecting the absolute conic. The new results include closed-form expressions for the camera intrinsic parameters in terms of the AQC, an algorithm to obtain the dual absolute quadric from the AQC using straightforward matrix operations, and an equally direct computation of a Euclidean-upgrading homography from the AQC. We also completely characterize the 6×6 matrices acting on lines which are induced by a spatial homography.

Several algorithmic possibilities arising from the AQC are systematically explored and analyzed in terms of efficiency and computational cost. Experiments include 3D reconstruction from real images.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Agapito, L., Hayman, E., Reid, I.: Self-calibration of rotating and zooming cameras. Int. J. Comput. Vis. 45, 107–127 (2001)

    Article  MATH  Google Scholar 

  2. Bartoli, A., Sturm, P.: The 3d line motion matrix and alignment of line reconstructions. Int. J. Comput. Vis. 57, 159–178 (2004)

    Article  Google Scholar 

  3. Bayro-Corrochano, E., Banarer, V.: A geometric approach for the theory and applications of 3d projective invariants. J. Math. Imaging Vis. 16(2), 131–154 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  4. Berger, M.: Geometry. Springer, Berlin (1987)

    Google Scholar 

  5. Bougnoux, S.: From projective to euclidean space under any practical situation, a criticism of self-calibration. In: Proc. International Conference on Computer Vision, Brisbane, Australia, pp. 790–796 (1998)

  6. Carlsson, S.: The double algebra: An effective tool for computing invariants in computer vision. In: Proc. of the Second Joint European-US Workshop on Applications of Invariance in Computer Vision, London, UK, 1994, pp. 145–164. Springer, Berlin (1994)

    Google Scholar 

  7. Faugeras, O.: What can be seen in three dimensions with an uncalibrated stereo rig. In: Proc. European Conference on Computer Vision, pp. 563–578 (1992)

  8. Faugeras, O.: Three Dimensional Computer Vision. MIT Press, Cambridge (1993)

    Google Scholar 

  9. Faugeras, O., Luong, Q.-T., Papadopoulou, T.: The Geometry of Multiple Images: The Laws That Govern The Formation of Images of A Scene and Some of Their Applications. MIT Press, Cambridge (2001)

    MATH  Google Scholar 

  10. Forsyth, D.A., Ponce, J.: Computer Vision: A Modern Approach. Prentice Hall, New York (2002)

    Google Scholar 

  11. Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2003)

    Google Scholar 

  12. Hartley, R.I.: Estimation of relative camera positions for uncalibrated cameras. In: Proc. European Conference on Computer Vision, London, UK, 1992, pp. 579–587. Springer, Berlin (1992)

    Google Scholar 

  13. Heyden, A.: Geometry and algebra of multiple projective transformations. PhD thesis, Lund University (1995)

  14. Heyden, A., Åström, K.: Euclidean reconstruction from image sequences with varying and unknown focal length and principal point. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, New York, USA (1997)

  15. Kahl, F., Triggs, B., Åström, K.: Critical motions for auto-calibration when some intrinsic parameters can vary. J. Math. Imaging Vis. 13(2), 131–146 (2000)

    Article  MATH  Google Scholar 

  16. Ma, Y., Soatto, S., Kosecka, J., Sastry, S.: An Invitation to 3-D Vision. Springer, Berlin (2005)

    Google Scholar 

  17. Maybank, S.J., Faugeras, O.D.: A theory of self-calibration of a moving camera. Int. J. Comput. Vis. 8(2), 123–151 (1992)

    Article  Google Scholar 

  18. Pollefeys, M., Gool, L.V.: A stratified approach to metric self-calibration. In: Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 407–412 (1997)

  19. Pollefeys, M., Koch, R., van Gool, L.: Self-calibration and metric reconstruction in spite of varying and unknown internal camera parameters. Int. J. Comput. Vis. 1(32), 7–25 (1999)

    Article  Google Scholar 

  20. Ponce, J.: On computing metric upgrades of projective reconstructions under the rectangular pixel assumption. In: Second European Workshop on 3D Structure from Multiple Images of Large-Scale Environments, London, UK, 2001, pp. 52–67. Springer, Berlin (2001)

    Google Scholar 

  21. Ponce, J., McHenry, K., Papadopoulo, T., Teillaud, M., Triggs, B.: On the absolute quadratic complex and its application to autocalibration. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, Washington, DC, USA, vol. 1, pp. 780–787 (2005)

  22. Pottman, H., Wallner, J.: Computational Line Geometry. Springer, New York (2001)

    Google Scholar 

  23. Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes in C: The Art of Scientific Computing, 2nd edn. Cambridge University Press, Cambridge (1993)

    Google Scholar 

  24. Ronda, J.I., Gallego, G., Valdés, A.: Camera autocalibration using Plücker coordinates. In: International Conference on Image Processing, Genoa, Italy, vol. 3, pp. 800–803 (2005)

  25. Semple, J.G., Kneebone, G.T.: Algebraic Projective Geometry. Oxford Classic Texts in the Physical Sciences. Clarendon, Oxford (1952)

    MATH  Google Scholar 

  26. Seo, Y., Heyden, A.: Auto-calibration from the orthogonality constraints. In: Proc. International Conference on Pattern Recognition, Los Alamitos, CA, USA, vol. 01, pp. 1067–1071 (2000)

  27. Seo, Y., Heyden, A., Cipolla, R.: A linear iterative method for auto-calibration using the dac equation. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, Barcelona, Spain, pp. 880–885 (2001)

  28. Trefethen, L.N., Bau, D.: Numerical Linear Algebra. SIAM, Philadelphia (1997)

    MATH  Google Scholar 

  29. Triggs, B.: Autocalibration and the absolute quadric. In: Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, Puerto Rico, USA, June 1997, pp. 609–614 (1997)

  30. Valdés, A., Ronda, J.: Camera autocalibration and the calibration pencil. J. Math. Imaging Vis. 23(2), 167–174 (2005)

    Article  Google Scholar 

  31. Valdés, A., Ronda, J.I., Gallego, G.: Linear camera autocalibration with varying parameters. In: Proc. International Conference on Image Processing, Singapore, vol. 5, pp. 3395–3398 (2004)

  32. Valdés, A., Ronda, J.I., Gallego, G.: The absolute line quadric and camera autocalibration. Int. J. Comput. Vis. 66(3), 283–303 (2006)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to José I. Ronda.

Additional information

This work has been partly supported by the Spanish Administration agencies CDTI and CICYT under projects CENIT-VISION 2007-1007 and TEC2007-67764 respectively.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ronda, J.I., Valdés, A. & Gallego, G. Line Geometry and Camera Autocalibration. J Math Imaging Vis 32, 193–214 (2008). https://doi.org/10.1007/s10851-008-0095-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10851-008-0095-0

Keywords