Using singular displacements for uncalibrated monocular visual systems

  • T. Viéville
  • D. Lingrand
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1065)


In the present paper, we review and complete the equations and the formalism which allow to achieve a minimal parameterization of the retinal displacement for a monocular visual system without calibration.

Considering the emergence of active visual systems for which we can not consider that the calibration parameters are either known or fixed, we develop an alternative strategy using the fact that certain class of special displacements induces enough equations to evaluate the calibration parameters, so that we can recover the affine or Euclidean structure of the scene when needed.

A synthesis of what can be recovered for singular displacements in terms of camera calibration, scene geometry and kinematics is proposed. We give, for the different levels of calibration, an exhaustive list of the geometric and kinematic information which can be recovered. Following a strategy based on special kind of displacements, such as fixed axis rotations or pure translations for instance, we describe how to detect this particular classes of displacement.


Structure and Motion Singular Displacements Self-Calibration 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    O. Faugeras. Three-dimensional Computer Vision: a geometric viewpoint. MIT Press, Boston, 1993.Google Scholar
  2. 2.
    D. Lingrand and T. Viéville. Dynamic foveal 3d sensing using affine models. Technical Report RR-2687, INRIA, 1995.Google Scholar
  3. 3.
    T. Luong. Matrice Fondamentale et Calibration Visuelle sur l'Environnement. PhD thesis, Université de Paris-Sud, Orsay, 1992.Google Scholar
  4. 4.
    P. Torr, A. Zisserman, and S. Maybank. Robust detection of degenerated configurations for the fundamental matrix. In 5th International Conference on Computer Vision, pages 1037–1042, 1995.Google Scholar
  5. 5.
    T. Viéville. Autocalibration of visual sensor parameters on a robotic head. Image and Vision Computing, 12, 1994.Google Scholar
  6. 6.
    T. Viéville, E. Clergue, R. Enciso, and H. Mathieu. Experimentating with 3D vision on a robotic head. Robotics and Autonomous Systems, 14(1), 1995.Google Scholar
  7. 7.
    T. Viéville and D. Lingrand. Using singular displacements for uncalibrated monocular visual systems. Technical Report RR-2678, INRIA, 1995.Google Scholar
  8. 8.
    T. Viéville, C. Zeller, and L. Robert. Using collineations to compute motion and structure in an uncalibrated image sequence. International Journal of Computer Vision, 1995. To appear.Google Scholar
  9. 9.
    C. Wiles and M. Brady. Closing the loop on multiple motion. In 5th International Conference on Computer Vision, pages 308–313, 1995.Google Scholar
  10. 10.
    R. Willson. Modeling and Calibration of Automated Zoom Lenses. PhD thesis, Department of Electical and Computer Engineering, Carnegie Mellon University, 1994.Google Scholar
  11. 11.
    C. Zeller and O. Faugeras. Applications of non-metric vision to some visual guided tasks. In The 12th Int. Conf. on Pattern Recognition, pages 132–136, 1994.Google Scholar
  12. 12.
    Z. Zhang, R. Deriche, Q.-T. Luong, and O. Faugeras. A robust approach to image matching: Recovery of the epipolar geometry. In Proc. International Symposium of Young Investigators on Information∖Computer∖Control, pages 7–28, Beijing, China, Feb. 1994.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • T. Viéville
    • 1
  • D. Lingrand
    • 1
  1. 1.INR1A, SophiaValbonneFrance

Personalised recommendations