MULTIPLE VIEW GEOMETRY FOR MIXED DIMENSIONAL CAMERAS

Kazuki Kozuka, Jun Sato

Abstract

In this paper, we analyze the multiple view geometry under the case where various dimensional imaging sensors are used together. Although the multiple view geometry has been studied extensively and extended for more general situations, all the existing multiple view geometries assume that the scene is observed by the same dimensional imaging sensors, such as 2D cameras. In this paper, we show that there exist multilinear constraints on image coordinates, even if the dimensions of camera images are different each other. The new multilinear constraints can be used for describing the geometric relationships between 1D line sensors, 2D cameras, 3D range sensors etc., and for calibrating mixed sensor systems.

References

  1. Faugeras, O. and Keriven, R. (1995). Scale-space and affine curvature. In Proc. Europe-China Workshop on Geometrical Modelling and Invariants for Computer Vision, pages 17-24.
  2. Faugeras, O. and Luong, Q. (2001). The Geometry of Multiple Images. MIT Press.
  3. Hartley, R. and Schaffalitzky, F. (2004). Reconstruction from projections using grassman tensors. In Proc. 8th European Conference on Computer Vision, volume 1, pages 363-375.
  4. Hartley, R. and Zisserman, A. (2000). Multiple View Geometry in Computer Vision. Cambridge University Press.
  5. Hayakawa, K. and Sato, J. (2006). Multiple View Geometry in the Space-Time. In Proc. 7th Asian Conference on Computer Vision, volume 2, pages 437-446.
  6. Heyden, A. (1998). A common framework for multiple view tensors. In Proc. 5th European Conference on Computer Vision, volume 1, pages 3-19.
  7. Shashua, A. and Wolf, L. (2000). Homography tensors: On algebraic entities that represent three views of static or moving planar points. In Proc. 6th European Conference on Computer Vision, volume 1, pages 507-521.
  8. Sturm, P. (2002). Mixing catadioptric and perspective cameras. In Proc. Workshop on Omnidirectional Vision.
  9. Sturm, P. (2005). Multi-view geometry for general camera models. In Proc. Conference on Computer Vision and Pattern Recognition, pages 206-212.
  10. Thirthala, S. and Pollefeys, M. (2005). Trifocal tensor for heterogeneous cameras. In Proc. Workshop on Omnidirectional Vision.
  11. Triggs, B. (1995). Matching constraints and the joint image. In Proc. 5th International Conference on Computer Vision, pages 338-343.
  12. Wexler, L. and Shashua, A. (2000). On the synthesis of dynamic scenes from reference views. In Proc. Conference on Computer Vision and Pattern Recognition.
  13. Wolf, L. and Shashua, A. (2001). On projection matrices Pk ? P2, k = 3, · · · , 6, and their applications in computer vision. In Proc. 8th International Conference on Computer Vision, volume 1, pages 412-419.
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Paper Citation


in Harvard Style

Kozuka K. and Sato J. (2008). MULTIPLE VIEW GEOMETRY FOR MIXED DIMENSIONAL CAMERAS . In Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008) ISBN 978-989-8111-21-0, pages 5-12. DOI: 10.5220/0001072500050012


in Bibtex Style

@conference{visapp08,
author={Kazuki Kozuka and Jun Sato},
title={MULTIPLE VIEW GEOMETRY FOR MIXED DIMENSIONAL CAMERAS},
booktitle={Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008)},
year={2008},
pages={5-12},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001072500050012},
isbn={978-989-8111-21-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008)
TI - MULTIPLE VIEW GEOMETRY FOR MIXED DIMENSIONAL CAMERAS
SN - 978-989-8111-21-0
AU - Kozuka K.
AU - Sato J.
PY - 2008
SP - 5
EP - 12
DO - 10.5220/0001072500050012