Abstract
In this paper we describe a flexible approach for determining the relative orientation of the camera with respect to the scene. The main premise of the approach is the fact that in man-made environments, the majority of lines is aligned with the principal orthogonal directions of the world coordinate frame. We exploit this observation towards efficient detection and estimation of vanishing points, which provide strong constraints on camera parameters and relative orientation of the camera with respect to the scene.
By combining efficient image processing techniques in the line detection and initialization stage we demonstrate that simultaneous grouping and estimation of vanishing directions can be achieved in the absence of internal parameters of the camera. Constraints between vanishing points are then used for partial calibration and relative rotation estimation. The algorithm has been tested in a variety of indoors and outdoors scenes and its efficiency and automation makes it amenable for implementation on robotic platforms.
This work is supported by NSF grant IIS-0118732 and George Mason University Research Initiative Grant.
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M. Antone and S. Teller. Automatic recovery of relative camera rotations for urban scenes. In IEEE Proceedings of CVPR, 2000.
B. Brillaut-O’Mahony. New method for vanishing point detection. CVGIP: Image Understanding, 54(2):289–300, September 1991.
B. Caprile and V. Torre. Using vanishing points for camera calibration. International Journal of Computer Vision, 3:127–140, 1990.
R. Collins. Vanishing point calculation as statistical inference on the unit sphere. In Proceedings of International Conference on Computer Vision, pages 400–403, 1990.
J. Coughlan and A. Yuille. Manhattan world: Compass direction from a single image by bayesian inference. In IEEE Proceedings International Conference on Computer Vision, pages 941–7, 1999.
R. Hartley and A. Zisserman. Multiple View Geometry in Computer Vision. Cambridge University Press, 2000.
D. Jelinek and C.J. Taylor. Reconstruction of linearly parametrized models from single images with camera of unknown focal lenght. IEEE Transactions of PAMI, pages 767–774, July 2001.
P. Kahn, L. Kitchen, and E.M. Riseman. A fast line finder for vision-guided robot navigation. IEEE Transactions on PAMI, 12(11):1098–1102, 1990.
K. Kanatani. Geometric Computation for Machine Vision. Oxford Science Publications, 1993.
D. Liebowitz and A. Zisserman. Combining scene and auto-calibration constraints. In Proceedings of International Conference on Computer Vision, 1999.
G. J. McLachlan and K. E. Basford. Mixture Models: Inference and Applications. Marcel Dekker Inc., N.Y., 1988.
L. Quan and R. Mohr. Determining perspective structures using hierarchical hough transforms. P.R. Letters, 9:279–286, 1989.
C. Rother. A new approach for vanishing point detection in architectural environments. In Proceedings of the British Machine Vision Conference, 2000.
T. Tuytelaars, M. Proesmans, and L. Van Gool. The cascaded Hough transform. In Proceedings of ICIP, pages 736–739, 1998.
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© 2002 Springer-Verlag Berlin Heidelberg
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Košecká, J., Zhang, W. (2002). Video Compass. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds) Computer Vision — ECCV 2002. ECCV 2002. Lecture Notes in Computer Science, vol 2353. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-47979-1_32
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DOI: https://doi.org/10.1007/3-540-47979-1_32
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