A Camera Self-Calibration Method Based on Plane Lattice and Orthogonality
The calibration using orthogonal line is one of the basic approaches of camera calibration, but it requires the orthogonal line be accurately detected, which makes results of error increases. This paper propose a novel camera self-calibration technique using plane lattices and virtual orthogonal line. The rigorous analytical relations among the feature point coordinates of the plane lattice, the corresponding image point coordinate, intrinsic parameters, relative pose are induced according to homography matrix of the central projection. Let a slope of non-parallel and non-orthogonal virtual line in the lattice plane, and the slope of its orthonormal line can be calculated. In at least three photographs taken, vanishing points can be solved in two groups of orthogonal directions by using the homography matrix, so the camera intrinsic parameters are linearly figured out. This method has both simple principle and convenient pattern manufacture, and does not involve image matching, besides having no requirement concerning camera motion. Simulation experiments and real data show that this algorithm is feasible, and provides a higher accuracy and robustness.
Hartley RI. Estimation of relative camera positions for uncalibrated cameras. In: Proceedings of the European Conference on Computer Vision. 1992; 1: 579-587.
Maybank SJ, Faugeras OD. A theory of self-calibration of a moving camera. The International Journal of Computer Vision. 1992; 8(2): 123-151.
Sturm PF, Maybank SJ. On plane-based camera calibration: A general algorithm, singularities, applications. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1999; 1: 432-437.
Tsai RY. An efficient and accurate camera calibration technique for 3D machine vision. Proc. IEEE Computer Vision and Pattern Recognition Conference. 1986; 1: 364-374.
Zhang Z. Camera calibration with one-dimensional objects. In: Proceedings of 7th European Conference on Computer Vision LNCS 2353. 2002; 1: 161-174.
Hou T, Niu H, Fan D. Speed control for high-speed railway on multi-mode intelligent control and feature recognition. Indonesian Journal of Electrical Engineering. 2012; 10(8): 2068-2074.
Xu ZY, LI Wb, Wang Yun, Luo C, Gao SY, Cao DD. Trinocular calibration method based on binocular calibration. Indonesian Journal of Electrical Engineering. 2012; 10(6): 1439-1444.
Hartley R, Zisserman A. Multiple view geometry in computer vision. Cambridge: University Press. 2000.
Li H, Wu FC, Hu ZY. A novel linear camera self-calibration technique. Chinese Journal of Computers. 2000; 23(11): 1121-1129.
Lei C, Wu FC, Hu ZY. Kruppa equation and camera self-calibration. Chinese Journal of Automation. 2001; 27(5): 621-630.
Wu FC, Li H, Hu ZY. A study on active vision based camera self-calibration. Chinese Journal of Automation. 2001; 27(6): 736-746.
Zhang Z. A flexible new technique for camera calibration. IEEE Transaction on Pattern Analysis and Machine Intelligence. 2000; 22(11): 1330-1334.
Meng XQ, Li H, Hu ZY. A new easy camera calibration technique based on circular points. In: Proceedings of the British Machine Vision Conference. 2000; 1: 496-501.
Wu FC, WANG GH, Hu ZY. A linear approach for determining intrinsic parameters and pose of cameras from rectangles. Journal of software, 2003; 14(2): 703-712.
Li XJ, Zhu HJ, Wu F. Camera calibration based on coplanar similar geometrical entities. PR&AI. 2004; 17(4): 457-461.
Wang G, Jonathan Wu QM, Zhang W. Kruppa equation based camera calibration from homography induced by remote plane. Pattern Recognition Letters. 2008; 29(16): 2137-2144.
Wang H, Zhao Y. A new planar circle-based approach for camera self-calibration. Journal of Computational Information Systems. 2010; 6(9): 2877-2883
Zhao Y, Wang H. Conic and circular points based camera linear calibration. Journal of Information and Coputational Science. 2010; 7(12): 2478-2485.
Zhao Y, Wang H, Wang XF. A conic-based approach for camera linear self-calibration. Journal of Information and Computational Science. 2010; 7(10): 1959-1966.
Wang H, Zhao Y, Li J. Innovation experiment based on circular points and Laguerre theorem in computer vision. In Proceedings of 2nd International Conference on Education Technology and Computer. 2010; 1: 25-28.
Duan HX, Wu YH. A calibration method for paracatadioptric camera from sphere images. Pattern Recognition Letters. 2012; 33(6): 677-684.
Puig L, Bermúdez J, Sturm P, Guerrero JJ. Calibration of omnidirectional cameras in practice: A comparison of methods. Computer Vision and Image Understanding. 2012; 116(1): 120-137.
Article MetricsAbstract view : 0 times
PDF - 0 times
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
TELKOMNIKA Telecommunication, Computing, Electronics and Control
ISSN: 1693-6930, e-ISSN: 2302-9293
Universitas Ahmad Dahlan, 4th Campus
Jl. Ringroad Selatan, Kragilan, Tamanan, Banguntapan, Bantul, Yogyakarta, Indonesia 55191
Phone: +62 (274) 563515, 511830, 379418, 371120 ext. 4902, Fax: +62 274 564604