Abstract
We describe a new method of achieving autocalibration that uses a stochastic optimization approach taken from the field of evolutionary computing and we perform a number of experiments on standardized data sets that show the effectiveness of the approach. The basic assumption of this method is that the internal (intrinsic) camera parameters remain constant throughout the image sequence, i.e. they are taken from the same camera without varying the focal length. We show that for the autocalibration of focal length and aspect ratio, the evolutionary method achieves comparable results without the implementation complexity of other methods. Autocalibrating from the fundamental matrix is simply transformed into a global minimization problem utilizing a cost function based on the properties of the fundamental matrix and the essential matrix.
Partially funded by Nortel Networks Scholarship.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
O. Faugeras and Q.-T. Luong, The Geometry of Multiple Images. The MIT Press, 2001.
R. Hartley and A. Zisserman, Multiple view geometry in computer vision. Cambridge University Press, 2000..[3] A. Fusiello, Uncalibrated Euclidean reconstruction: a review," Image and Vision Computing, vol. 18, pp. 555–563, 2000.
A. Whitehead and G. Roth, The Projective Vision Toolkit, in Proceedings Modelling and Simulation, (Pittsburgh, Pennsylvania), May 2000.
R. Hartley, “Kruppa’s equations deri ved from the fundamental matrix,” IEEE Trans. On Pattern Analysis and Machine Intelligence, vol. 19, pp. 133–135, February 1997.
Q.-T. Luong and O.D. Faugeras, “Self-calibration of a moving camera from point correspondences and fundamental matrices,” International Journal of Computer Vision, vol. 22, no. 3, pp. 261–289, 1997.
L. Lourakis and R. Deriche, “Camera self-calibration using the svd of the fundamental matrix,”Tech. Rep. 3748, INRIA, Aug. 1999.
P. Mendonca and R. Cipolla, “A simple technique for self-calibration,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, (Fort Collins, Colorado), pp. 112–116, June 1999.
C. Zeller and O. Faugeras, “Camera self-calibration from video sequences: the kruppa equations revisited,” Tech. Rep. 2793, INRIA, Feb. 1996.
M. Pollefeys, R. Koch, and L. V. Gool, “Self calibration and metric reconstruction in spite of varying and unknown internal camera parameters,” in International Conference on Computer Vision, pp. 90–96, 1998.
M. Pollefeys, Self-calibration and metric 3d reconstruction from uncalibrated image sequences. PhD thesis, Catholic University Leuven, 1999.
M. Pollefeys, R. Koch, and L. V. Gool, “Self-calibration and metric reconstruction in spite of varying and unknown intrinsic camera parameters,” International Journal of Computer Vision, vol. 32, no. 1, pp. 7–25, 1999.
P. Sturm, A case against kruppa’s equations for camera self-calibration,” IEEE Trans. On Pattern Analysis and Machine Intelligence, vol. 22, pp. 1199–1204, Oct. 2000.
S. Bougnoux, “From projecti ve to Euclidean space under any practical situation, a criticism of self-calibration,” in Proc. 6th Int. Conf. on Computer Vision, (Bombay, India), pp. 790–796, 1998.
W. H. Press and B. P. Flannery, Numerical recipes in C. Cambridge university press, 1988.
M. Maza and D. Yuret, “Dynamic hill climbing,” AI Expert, pp. 26–31, 1994.
R. Hartley, “In defense of the 8 point algorithm,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 6, 1997.
A. Morgan, Solving polynomial systems using continuation for science and engineering. Prentice Hall, Englewoord Clifis, 1987
G. Roth and A. Whitehead “Using projective vision to find camera positions in an image sequence,” in Vision Interface 2000, (Montreal, Canada), pp. 87–94, May 2000.
T. Ueshiba and F. Tomita, “A factorization method for projective and Euclidea reconstruction,” in ECCV’98, 5th European Conference on Computer Vision, (Freiburg, Germany), pp. 290–310, Springer Verlag, June 1998.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Whitehead, A., Roth, G. (2002). Evolutionary Based Autocalibration from the Fundamental Matrix. In: Cagnoni, S., Gottlieb, J., Hart, E., Middendorf, M., Raidl, G.R. (eds) Applications of Evolutionary Computing. EvoWorkshops 2002. Lecture Notes in Computer Science, vol 2279. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46004-7_29
Download citation
DOI: https://doi.org/10.1007/3-540-46004-7_29
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-43432-0
Online ISBN: 978-3-540-46004-6
eBook Packages: Springer Book Archive