Skip to main content
Log in

Hybrid homographies and fundamental matrices mixing uncalibrated omnidirectional and conventional cameras

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

In this paper, we present a deep analysis of the hybrid two-view relations combining images acquired with uncalibrated central catadioptric systems and conventional cameras. We consider both, hybrid fundamental matrices and hybrid planar homographies. These matrices contain useful geometric information. We study three different types of matrices, varying in complexity depending on their capacity to deal with a single or multiple types of central catadioptric systems. The first and simplest one is designed to deal with para-catadioptric systems, the second one and more complex considers the combination of a perspective camera and any central catadioptric system. The last one is the complete and generic model which is able to deal with any combination of central catadioptric systems. We show that the generic and most complex model sometimes is not the best option when we deal with real images. Simpler models are not as accurate as the complete model in the ideal case, but they provide a better and more accurate behavior in the presence of noise, being simpler and requiring less correspondences to be computed. Experiments with simulated data and real images are performed. To show the potential of these approaches, we develop two applications. The first is the successful matching between perspective images and hyper-catadioptric images using SIFT descriptors. In the second one, using only the hybrid fundamental matrix and the hybrid planar homography we compute the metric localization of the perspective camera inside the catadioptric view in an indoors environment.

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

Access this article

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Baker S., Nayar S.: A theory of single-viewpoint catadioptric image formation. Int. J. Comput. Vis. 35(2), 175–196 (1999)

    Article  Google Scholar 

  2. Barreto, J.P., Daniilidis, K.: Epipolar geometry of central projection systems using veronese maps. In: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1258–1265. NY (2006)

  3. Bartoli A., Sturm P.: Non-linear estimation of the fundamental matrix with minimal parameters. IEEE Trans. Pattern Anal. Mach. Intell. 26(4), 426–432 (2004)

    Article  Google Scholar 

  4. Bastanlar, Y., Temizel, A., Yardimci, Y., Sturm, P.: Effective structure-from-motion for hybrid camera systems. In: International Conference on Pattern Recognition, vol. 0, pp. 1654–1657. IEEE Computer Society, Los Alamitos, CA (2010)

  5. Chen, D., Yang, J.: Image registration with uncalibrated cameras in hybrid vision systems. In: Seventh IEEE Workshops on Application of Computer Vision. WACV/MOTIONS’05, vol. 1, pp. 427–432 (2005)

  6. Chen, X., Yang, J., Waibel, A.: Calibration of a hybrid camera network. In: Proceedings of Ninth IEEE International Conference on Computer Vision, vol. 1, pp. 150–155 (2003)

  7. Claus, D., Fitzgibbon, A.W.: A rational function lens distortion model for general cameras. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 213–219 (2005)

  8. Gasparini, S., Sturm, P., Barreto, J.: Plane-based calibration of central catadioptric cameras. In: Proceedings of the International Conference on Computer Vision, Kyoto, Japan (2009)

  9. Geyer, C., Daniilidis, K.: A unifying theory for central panoramic systems and practical applications. In: European Conference on Computer Vision, vol. 2, pp. 445–461 (2000)

  10. Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge. ISBN: 0521623049 (2000)

  11. Jankovic, N., Naish, M.: A centralized omnidirectional multi-camera system with peripherally-guided active vision and depth perception. In: IEEE International Conference on Networking, Sensing and Control, pp. 662–667, 15–17 April 2007

  12. Lin, H.Y., Wang, M.L.: Generalized stereo for hybrid omnidirectional and perspective imaging. In: IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops), pp. 2220–2227 (2009)

  13. Lowe D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 20, 91–110 (2004)

    Article  Google Scholar 

  14. Menem, M., Pajdla, T.: Constraints on perspective images and circular panoramas. In: Proceedings of the 15th British Machine Vision Conference. BMVA, British Machine Vision Association, London, UK (2004)

  15. Murillo, A.C., Guerrero, J.J., Sagües, C.: Surf features for efficient robot localization with omnidirectional images. In: IEEE International Conference on Robotics and Automation, Roma (2007)

  16. Puig, L., Guerrero, J., Sturm, P.: Matching of omindirectional and perspective images using the hybrid fundamental matrix. In: Proceedings of the Workshop on Omnidirectional Vision, Camera Networks and Non-Classical Cameras, Marseille, France (2008)

  17. Puig, L., Guerrero, J.J.: Self-location from monocular uncalibrated vision using reference omniviews. In: IROS 2009: The 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems. St. Louis, MO, USA (2009)

  18. Sagues C., Murillo A.C., Escudero F., Guerrero J.J.: From lines to epipoles through planes in two views. Pattern Recogn. 39(3), 384–393 (2006)

    Article  Google Scholar 

  19. Sturm, P.: Mixing catadioptric and perspective cameras. In: Workshop on Omnidirectional Vision, Copenhagen, Denmark, pp. 37–44 (2002)

  20. Sturm, P., Barreto, J.: General imaging geometry for central catadioptric cameras. In: Proceedings of the 10th European Conference on Computer Vision, Marseille, France, vol. 4, pp. 609–622. Springer, Berlin (2008)

  21. Svoboda T., Pajdla T.: Epipolar geometry for central catadioptric cameras. Int. J. Comput. Vis. 49(1), 23–37 (2002)

    Article  MATH  Google Scholar 

  22. Mauthner, T., Fraundorfer, F., Bischof, H.: Region matching for omnidirectional images using virtual camera planes. In: Proceedings of the 11th Computer Vision Winter Workshop 2006, Telc, Czech Republic, pp. 93–98 (2006)

  23. Vedaldi, A.: An open implementation of the SIFT detector and descriptor. Tech. Rep. 070012, UCLA CSD (2007)

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Luis Puig.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Puig, L., Sturm, P. & Guerrero, J.J. Hybrid homographies and fundamental matrices mixing uncalibrated omnidirectional and conventional cameras. Machine Vision and Applications 24, 721–738 (2013). https://doi.org/10.1007/s00138-012-0424-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-012-0424-6

Keywords

Navigation