Robust method for camera self-calibration by an unkown planar scene

Article Sidebar


PDF

Published:
Jun 15, 2014
Issue
Vol. 23 No. 1/2 (2014)
Section
Original research papers
CitedBy/Share
Crossref
Scopus
Google Scholar
Europe PMC

Main Article Content

A. Baataoui
LIIAN, Department of Mathematics and Computer Science, Faculty of Sciences, Dhar El Mehraz, Sidi Mohamed Ben Abdellah University, B.P 1796, Atlas, Fez, Morocco.
N. El Akkad
LIIAN, Department of Mathematics and Computer Science, Faculty of Sciences, Dhar El Mehraz, Sidi Mohamed Ben Abdellah University, B.P 1796, Atlas, Fez, Morocco.
Med. Masrar
LIIAN, Department of Mathematics and Computer Science, Faculty of Sciences, Dhar El Mehraz, Sidi Mohamed Ben Abdellah University, B.P 1796, Atlas, Fez, Morocco.
A. Saaidi
LIIAN, Department of Mathematics and Computer Science, Faculty of Sciences, Dhar El Mehraz, Sidi Mohamed Ben Abdellah University, B.P 1796, Atlas, Fez, Morocco.
K. Satori
LIIAN, Department of Mathematics and Computer Science, Faculty of Sciences, Dhar El Mehraz, Sidi Mohamed Ben Abdellah University, B.P 1796, Atlas, Fez, Morocco.

DOI: https://doi.org/10.22630/MGV.2014.23.1.3
Keywords : self-calibration, equilateral triangle, absolute conic, homography, varying intrinsic parameters
Abstract
In this paper we present a self-calibration method for a CCD camera with varying intrinsic parameters based on an unknown planar scene. The advantage of our method is reducing the number of images (two images) needed to estimate the parameters of the camera used. Moreover, self-calibration equations are related to the number of points matched (very numerous and easy to detect) rather than to the number of images, since the use of a large number of images requires high computation time. On the other hand, we base on the points matched, which are numerous, when estimating the projection matrices and homographies between the images. The latter are used with the images of the absolute conic to formulate a system of non-linear equations (self-calibration equations depend on the number of matched pairs). Finally, the intrinsic parameters of the camera can be obtained by minimizing a non-linear cost function in a two-step procedure: initialization and optimization. Experiment results show the robustness of our algorithms in terms of stability and convergence.

Article Details

How to Cite
Baataoui, A., El Akkad, N., Masrar, M., Saaidi, A., & Satori, K. (2014). Robust method for camera self-calibration by an unkown planar scene. Machine Graphics and Vision, 23(1/2), 37–57. https://doi.org/10.22630/MGV.2014.23.1.3
References

J. More: The Levenberg-Marquardt Algorithm, Implementation and Theory. In G. A. Watson, editor, Numerical Analysis, Lecture Notes in Mathematics 630. Springer-Verlag, pp. 105-116, 1977. (Crossref)

M. A. Fischler and R. C. Bolles: Random sample consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Graphics and Image Processing, pp. 381-395, 1981. (Crossref)

C. Harris and M. Stephens: A combined Corner et Edge Detector. 4th Alvey vision Conference. pp. 147-151, 1988. (Crossref)

O. Faugeras, Q.-T. Luong, S. Maybank: Camera Self-Calibration: Theory and Experiments . In: G. Sandini (Ed.), Proceedings of the European Conference on Computer Vision, Springer-Verlag, pp. 321-334, 1992. (Crossref)

S. J. Maybank and O. D. Faugeras: A Theory of Self Calibration of a Moving Camera. International Journal on Computer Vision, pp. 123-151, 1992. (Crossref)

R. I. Hartley: Self-Calibration From Multiple Views with a Rotating Camera. In ECCV, pp. 471-478, 1994. (Crossref)

A. Heyden, K. Aström: Euclidean Reconstruction from Constant Intrinsic Parameters. In: Proc. International Conference on Pattern Recognition, pp. 339-343, 1996. (Crossref)

M. Pollefeys, L.V. Gool, M. Oosterlinck: The Modulus Constraint: A New Constraint for Self-Calibration. In: Proc. Int. Conf. on Pattern Recognition, pp. 349-353, 1996. (Crossref)

S. M. Smith and J. M. Brady: SUSAN - A New Approach to Low Level Image Processing. International Journal of Computer Vision, 45-78, 1997. (Crossref)

M. Pollefeys, R. Koch and L. Van Gool: Self-Calibration and Metric Reconstruction in Spite of Varying and Unknown Internal Camera Parameters. ICCV, pp. 90-95, 1998.

B. Triggs: Autocalibration from Planar Sequences. In Proceedings of 5th European Conference on Computer Vision, pp. 89-105, 1998. (Crossref)

P. F. Sturm and S. J. Maybank: On Plane-Based Camera Calibration: A General Algorithm, Singularities, Applications. In Proceedings of the CVPR-IEEE, pp. 432-437, 1999.

P. Sturm: A Case Against Kruppa’s Equations for Camera Self-Calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1199-1204, 2000. (Crossref)

Z. Zhang: A Flexible New Technique for camera Calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence. pp. 1330-1334, 2000. (Crossref)

M. Wilczkowiak, E. Boyer, P. Sturm: Camera Calibration and 3D Reconstruction from Single Images Using Parallelepipeds. In ICCV, Vancouver, pp. 142-148, 2001.

M. Lhuillier and L. Quan: Quasi-Dense Reconstruction from Image Sequence. ECCV, 2002. (Crossref)

P. Sturm: Critical motion sequences for the self-calibration of cameras and stereo systems with variable focal length. Image and Vision Computing, 20(5-6):415-426, 2002. (Crossref)

P. Gurdjos and P. Sturm: Methods and Geometry for Plane-Based Self-Calibration. CVPR, pp. 491-496, 2003.

P. Liu, J.Shi, J. Zhou and L. Jiang: Camera Self-Calibration Using the Geometric Structure in Real Scenes. In: Proceedings of the Computer Graphics International, pp. 262-265, 2003.

D. G. Lowe: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision, 91-110, 2004. (Crossref)

B. Bouda, Lh. Masmoudi and D. Aboutajdine. A New Grey Level Corner Detection Based on Electrostatic Model. ICGST-GVIP, 21-26, 2006. (Crossref)

A. Saaidi, H. Tairi and K. Satori: Fast Stereo Matching Using Rectification and Correlation Techniques. ISCCSP, 2nd International Symposium on Communications, Control and Signal Processing, 2006.

Y. Li, Y.S. Hung, S. Lee: A Stratified Self-Calibration Method for Circular Motion in Spite of Varying Intrinsic Parameters. Image and Vision Computing, pp. 731-739, 2008. (Crossref)

A. Saaidi, A. Halli, H. Tairi and K. Satori: Self -Calibration Using a Particular Motion of Camera. In WSEAS Transaction on Computer Research, pp. 295-299, 2008.

J. Kima, I. Kweon: Camera calibration based on arbitrary parallelograms. Computer Vision and Image Understanding, pp. 1-10, 2009 (Crossref)

A. Saaidi, A. Halli, H. Tairi and K. Satori: Self-Calibration Using a Planar Scene and Parallelogram. ICGST-GVIP, pp. 41-47, 2009.

A. El-attar, M. Karim, H. Tairi, S. Ionita: A Robust Multistage Algorithm for Camera Self-Calibration Dealing with Varying Intrinsic Parameters. JATIT, pp. 46-54, 2011.

A. Baataoui, I. El Batteoui, A. Saaidi and K. Satori: Camera Self-Calibration by an Equilateral Triangle. International Journal of Computer Applications, pp. 29-34, 2012.

Z. Jiang, S. Liu: Self-calibration of Varying Internal Camera Parameters Algorithm Based on Quasi-affine Reconstruction. Journals of Computers, pp. 774-778, 2012. (Crossref)

Y. Shang, Z. Yue, M. Chen and Q. Song: A New Method of Camera Self-Calibration Based on Relative Lengths. Information Technology Journal, pp. 376-379, 2012. (Crossref)

N. El Akkad, M. Merras, A. Saaidi, and K. Satori: Robust Method for Self-Calibration of Cameras Having the Varying Intrinsic Parameters. JATIT, pp. 57-67, 2013. (Crossref)

Statistics

Downloads

Download data is not yet available.
Recommend Articles