Conic Based Camera Re-calibration after Zooming

  • Iuri Frosio
  • Cristina Turrini
  • Alberto Alzati
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8156)


We describe here a method to compute the internal parameters of a camera whose position and orientation are known. The method is based on the observation of at least three conics on a known plane; these can be easily extracted in a real scenario from a tiled floor or other regular structures. The method estimates the principal point and focal length using a unique image of the conics when these are observed by an additional calibrated camera. Differently from other methods, no assumption is made on the conics used for calibration. The experimental results demonstrate that the accuracy of the method is comparable to that of more traditional (and time consuming) approaches. It can find applications in systems of Pan-Zoom-Tilt (PZT) or traditional cameras, that are nowadays widely employed, for instance in the surveillance domain, and require frequent re-calibration.


camera calibration conics computer vision surveillance 


  1. 1.
    Zhang, Z.: Camera Calibration. In: Medioni, G., Kang, S.B. (eds.) Emerging Topics in Computer Vision, ch. 2, pp. 4–43. Prentice Hall Professional Technical Reference (2004)Google Scholar
  2. 2.
    Zhang, Z.: Flexible Camera Calibration by Viewing a Plane from Unknown Orientations. In: ICCV 1999 (1999)Google Scholar
  3. 3.
    Bouguet, J.-Y.: Camera Calibration Toolbox for Matlab,
  4. 4.
    Abado, F., Camahort, E., Vivó, R.: Camera Calibration Using Two Concentric Circles. In: International Conference on Image Analysis and Recognition, pp. 688–696 (2004)Google Scholar
  5. 5.
    Ying, X., Zha, H.: Camera calibration using principal-axes aligned conics. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds.) ACCV 2007, Part I. LNCS, vol. 4843, pp. 138–148. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  6. 6.
    Yang, C., Sun, F., Hu, Z.: Planar conic based camera calibration. In: The 15th International Conference on Pattern Recognition, vol. 1, pp. 555–558 (2000)Google Scholar
  7. 7.
    Frosio, I., Alzati, A., Bertolini, M., Turrini, C., Borghese, N.A.: Linear pose estimate from corresponding conics. Pattern Recognition 45, 4169–4181 (2012)CrossRefGoogle Scholar
  8. 8.
    Kahl, F., Heyden, A.: Using Conic Correspondences in Two Images to Estimate the Epipolar Geometry. In: Int. Conf. on Computer Vision, pp. 761–766 (1998)Google Scholar
  9. 9.
    Borghese, N.A., Colombo, F.M., Alzati, A.: Computing Camera Focal Length by Zooming a Single Point. Pattern Recognition 39, 1522–1529 (2006)CrossRefGoogle Scholar
  10. 10.
    Faugeras, O.: Three-dimensional computer vision: a geometric viewpoint. MIT Press (1993)Google Scholar
  11. 11.
    Nikon website,
  12. 12.
    Canon website,
  13. 13.
    Fraser, C.S., Ajlouni, A.S.S.: Zoom-dependent camera calibration in digital close-range photogrammetry. Photogrammetric Engineering & Remote Sensing 72(9), 1017–1026 (2006)CrossRefGoogle Scholar
  14. 14.
    Sun, X., Sun, J., Zhang, J., Li, M.: Simple zoom-lens digital camera calibration method based on exif. In: Three-Dimensional Image Capture and Applications VI, vol. 79 (2004)Google Scholar
  15. 15.
    Calore, E., Frosio, I.: Perspective correction in digital photography using on board accelerometer. Submitted to Image and Vision Computing (2013)Google Scholar
  16. 16.
    Adi, B.-I., Greville, T.N.E.: Generalized inverses. Springer, New York (2003)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Iuri Frosio
    • 1
  • Cristina Turrini
    • 2
  • Alberto Alzati
    • 2
  1. 1.Computer Science Dept.University of MilanItaly
  2. 2.Mathematics Dept.University of MilanItaly

Personalised recommendations