Advertisement

Extrinsic Camera Parameter Recovery from Multiple Image Sequences Captured by an Omni-Directional Multi-camera System

  • Tomokazu Sato
  • Sei Ikeda
  • Naokazu Yokoya
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3022)

Abstract

Recently, many types of omni-directional cameras have been developed and attracted much attention in a number of different fields. Especially, the multi-camera type of omni-directional camera has advantages of high-resolution and almost uniform resolution for any direction of view. In this paper, an extrinsic camera parameter recovery method for a moving omni-directional multi-camera system (OMS) is proposed. First, we discuss a perspective n-point (PnP) problem for an OMS, and then describe a practical method for estimating extrinsic camera parameters from multiple image sequences obtained by an OMS. The proposed method is based on using the shape-from-motion and the PnP techniques.

Keywords

Natural Feature Camera Parameter Angle Error Real Scene Projection Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Miyamoto, K.: Fish Eye Lens. Jour. of Optical Society of America 54(2), 1060–1061 (1964)CrossRefGoogle Scholar
  2. 2.
    Yamazawa, K., Yagi, Y., Yachida, M.: Omnidirectional Imaging with Hyperboloidal Projection. In: Proc. Int. Conf. on Intelligent Robots and Systems, vol. 2, pp. 1029–1034 (1993)Google Scholar
  3. 3.
    Nayar, S.K.: Catadioptic Omnidirectional Cameras. In: Proc. IEEE Int. Conf. on Computer Vision and Pattern Recognition, pp. 482–488 (1997)Google Scholar
  4. 4.
    Shimamura, J., Takemura, H., Yokoya, N., Yamazawa, K.: Construction and Presentation of a Virtual Environment Using Panoramic Stereo Images of a Real Scene and Computer Graphics Models. In: Proc. 15th IAPR Int. Conf. on Pattern Recognition, vol. IV, pp. 463–467 (2000)Google Scholar
  5. 5.
    Tanahashi, H., Yamamoto, K., Wang, C., Niwa, Y.: Development of a Stereo Omni-directional Imaging System(SOS). In: Proc. IEEE Int. Conf. on Industrial Electronics, Control and Instrumentation, pp. 289–294 (2000)Google Scholar
  6. 6.
    Ikeda, S., Sato, T., Yokoya, N.: High-resolution Panoramic Movie Generation from Video Streams Acquired by an Omnidirectional Multi-camera System. In: Proc. IEEE Int. Conf. on Multisensor Fusion and Integration for Intelligent System, pp. 155–160 (2003)Google Scholar
  7. 7.
    Horand, R., Conio, B., Leboullex, O.: An Analytic Solution for the Perspective 4-Point Problem. Computer Vision, Graphics, and Image Processing 47, 33–44 (1989)CrossRefGoogle Scholar
  8. 8.
    Yuan, J.S.C.: A General Photogrammetric Method for Determining Object Position and Orientation. IEEE Trans. on Robotics and Automation 5(2), 129–142 (1989)CrossRefGoogle Scholar
  9. 9.
    Krishnan, R., Sommer, H.J.: Monocular Pose of a Rigid Body Using Point Landmarks. Computer Vision and Image Understanding 55, 307–316 (1992)zbMATHGoogle Scholar
  10. 10.
    Klette, R., Schluns, K., Koschan, A. (eds.): Computer Vision: Three-dimensional Data from Image. Springer, Heidelberg (1998)zbMATHGoogle Scholar
  11. 11.
    Chen, C.S., Chang, W.Y.: Pose Estimation for Generalized Imaging Device via Solving Non-perspective N Point Problem. In: Proc. IEEE Int. Conf. on Robotics and Automation, pp. 2931–2937 (2002)Google Scholar
  12. 12.
    Beardsley, P., Zisserman, A., Murray, D.: Sequential Updating of Projective and Affine Structure from Motion. Int. Jour. of Computer Vision 23(3), 235–259 (1997)CrossRefGoogle Scholar
  13. 13.
    Tomasi, C., Kanade, T.: Shape and Motion from Image Streams under Orthography: A Factorization Method. Int. Jour. of Computer Vision 9(2), 137–154 (1992)CrossRefGoogle Scholar
  14. 14.
    Pollefeys, M., Koch, R., Vergauwen, M., Deknuydt, A.A., Gool, L.J.V.: Threedimentional Scene Reconstruction from Images. In: Proc. SPIE, vol. 3958, pp. 215–226 (2000)Google Scholar
  15. 15.
    Gluckman, J., Nayer, S.: Ego-motion and Omnidirectional Cameras. In: Proc. 6th Int. Conf. on Computer Vision, pp. 999–1005 (1998)Google Scholar
  16. 16.
    Etoh, M., Aoki, T., Hata, K.: Estimation of Structure and Motion Parameters for a Roaming Robot that Scans the Space. In: Proc. 7th Int. Conf. on Computer Vision, vol. I, pp. 579–584 (1999)Google Scholar
  17. 17.
    Taylor, C.J.: VideoPlus. In: Proc. IEEEWorkshop on Omnidirecitonal Vision, pp. 3–10 (2000)Google Scholar
  18. 18.
    Triggs, B., McLauchlan, P., Hartley, R., Fitzgibbon, A.: Bundle Adjustment a Modern Synthesis. In: Proc. Int. Workshop on Vision Algorithms, pp. 298–372 (1999)Google Scholar
  19. 19.
    Kanatani, K.: Statistical Optimization for Geometric Computation: Theory and Practice. Elsevier Science, Amsterdam (1998)Google Scholar
  20. 20.
    Sato, T., Kanbara, M., Yokoya, N., Takemura, H.: Dense 3-D Reconstruction of an Outdoor Scene by Hundreds-baseline Stereo Using a Hand-held Video Camera. Int. Jour. of Computer Vision 47(1-3), 119–129 (2002)zbMATHCrossRefGoogle Scholar
  21. 21.
    Harris, C., Stephens, M.: A Combined Corner and Edge Detector. In: Proc. Alvey Vision Conf., pp. 147–151 (1988)Google Scholar
  22. 22.
    Fischler, M.A., Bolles, R.C.: Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Communications of the ACM 24(6), 381–395 (1981)CrossRefMathSciNetGoogle Scholar
  23. 23.
    Point Gray Research Inc.: Ladybug, http://www.ptgrey.com/products/ladybug/index.html

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Tomokazu Sato
    • 1
  • Sei Ikeda
    • 1
  • Naokazu Yokoya
    • 1
  1. 1.Graduate School of Information ScienceNara Institute of Science and TechnologyNaraJapan

Personalised recommendations