Abstract
Recently, 3D structure recovery through self-calibration of camera has been actively researched. Traditional calibration algorithm requires known 3D coordinates of the control points while self-calibration only requires the corresponding points of images, thus it has more flexibility in real application. In general, self-calibration algorithm results in the nonlinear optimization problem using constraints from the intrinsic parameters of the camera. Thus, it requires initial value for the nonlinear minimization. Traditional approaches get the initial values assuming they have the same intrinsic parameters while they are dealing with the situation where the intrinsic parameters of the camera may change. In this paper, we propose new initialization method using the minimum 2 images. Proposed method is based on the assumption that the least violation of the camera’s intrinsic parameter gives more stable initial value. Synthetic and real experiment shows this result.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Faugeras, O., Luong, Q.-T., Maybank, S.: Camera Calibration: Theory and experiments. In: European Conference on Computer Vision, pp. 321–334 (1992)
Hartley, R.: Euclidean reconstruction from uncalibrated views. In: Mundy, J.L., Zisserman, A., Forsyth, D. (eds.) AICV 1993. LNCS, vol. 825, Springer, Heidelberg (1994)
Pollefeys, R., Van Gool, R., Oosterlinck, A.: The Modulus Constraint: A New Constraint for Self-Calibration. In: International Conference on Pattern Recognition, pp. 349–353 (1996)
Heyden, A., Astrom, K.: Euclidean Reconstruction from Constant Intrinsic Parameters. In: International Conference on Pattern Recognition (1996)
Triggs, B.: The Absolute Quadric. In: Proc. Computer Vision and Pattern Recognition (1997)
Pollefeys, M., Van Gool, L.: Self-calibration from the absolute conic on the plane at infinity. In: Sommer, G., Daniilidis, K., Pauli, J. (eds.) CAIP 1997. LNCS, vol. 1296, Springer, Heidelberg (1997)
Heyden, A., Astrom, K.: Euclidean Reconstruction from Image Sequences with Varying and Unknown Focal Length and Principal Point. In: Proc. CVPR (1997)
Bougnoux, S.: From Projective to Euclidean Space under any practical situation, a criticism of self-calibration. In: Proc. ICCV, pp. 790–796 (1998)
Pollefeys, M., Koch, R., Van Gool, L.: Self-calibration and Metric Reconstruction in Spite of Varying and Unknown Internal Camera Parameters. In: Proc. ICCV, pp. 90–95 (1998)
Faugeras, O.: What can be seen in three dimensions with an uncalibrated stereo rig? In: Sandini, G. (ed.) ECCV 1992. LNCS, vol. 588, pp. 563–578. Springer, Heidelberg (1992)
Hartley, R., Gupta, R., Chang, T.: Stereo from uncalibrated cameras. In: Proc. CVPR, pp. 761–764 (1992)
Ha, J.E., Yang, J.Y., Yoon, K.J., Kweon, I.S.: Self-calibration using the linear projective reconstruction. In: Proceedings of the International Conference on Robotics and Automation, pp. 885–890 (2000)
Hartley, R.: In defence of the 8-point algorithm. In: Fifth International Conference on Computer Vision, pp. 1064–1070 (1995)
Hartley, R., Sturm, P.: Triangulation. Computer Vision and Image Understanding 68, 146–157 (1997)
Rothwell, C., Faugeras, O., Csurka, G.: A comparison of projective reconstruction methods for pairs of views. Computer Vision and Image Understanding 68, 36–58 (1997)
Csurka, G., Horaud, R.: Finding the collineation between two projective reconstructions. INRIA RR-3468 (1998)
Tsai, R.Y.: A versatile camera calibration technique for high accuracy 3D machine vision metrology using off-the-self TV cameras and lenses. IEEE Journal of Robotics and Automation 3 (1987)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ha, JE., Kang, DJ. (2004). Initialization Method for the Self-Calibration Using Minimal Two Images. In: Laganá, A., Gavrilova, M.L., Kumar, V., Mun, Y., Tan, C.J.K., Gervasi, O. (eds) Computational Science and Its Applications – ICCSA 2004. ICCSA 2004. Lecture Notes in Computer Science, vol 3046. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24768-5_98
Download citation
DOI: https://doi.org/10.1007/978-3-540-24768-5_98
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22060-2
Online ISBN: 978-3-540-24768-5
eBook Packages: Springer Book Archive