基于空间共线点的单光心反射折射摄像机标定

段福庆; 吕科; 周明全

doi:10.3724/SP.J.1004.2011.01296

基于空间共线点的单光心反射折射摄像机标定

doi: 10.3724/SP.J.1004.2011.01296

1.
北京师范大学信息科学与技术学院北京 100875;
2.
中国科学院研究生院计算与通信工程学院北京 100049

详细信息

通讯作者:
段福庆博士,北京师范大学信息学院副教授.主要研究方向为摄像机标定,模式识别与机器学习. E-mail: fqduan@bnu.edu.cn

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出版历程
- 收稿日期: 2010-10-22
- 修回日期: 2011-06-05
- 刊出日期: 2011-11-20

Central Catadioptric Camera Calibration Based on Collinear Space Points

1.
College of Information Science and Technology, Beijing Normal University, Beijing 100875;
2.
College of Computing and Communication Engineering, Graduate University of Chinese Academy of Sciences, Beijing 100049

摘要

摘要: 一条空间直线的单光心反射折射图像是一个二次曲线段, 大多数利用直线进行单光心反射折射摄像机标定的方法都需要对直线的像进行二次曲线拟合, 曲线拟合的精度严重影响着标定的精度. 然而, 一条空间直线的像仅占整个二次曲线的一小段, 这使得曲线拟合的效果非常差. 本文利用空间三个共线点的反射折射投影给出了摄像机内参数的一个非线性约束. 当反射镜面为抛物面时, 在主点已知的情况下, 该约束变为线性约束. 如其他参数已知, 该约束变为关于有效焦距的多项式约束. 由此, 本文提出了三种不同条件下的标定算法, 算法中无需对直线的像进行二次曲线拟合, 无需场景的任何信息, 标定精度较高. 实验验证了算法的有效性.
- 单光心反射折射摄像机 /
- 摄像机标定 /
- 摄像机内参数 /
- 球成像模型
Abstract: For central catadioptric cameras, the projection of a space line is a conic segment, and most of the calibration methods using lines need conic fitting of line images, which highly affects the calibration accuracy. However, generally only a small segment of the conic is visible, which makes the conic fitting error-prone. In this paper, we derive a nonlinear constraint on all camera intrinsic parameters from the projections of any three collinear space points. With the principal point known, the constraint becomes linear for para-catadioptric cameras. With all other parameters known, the constraint becomes a polynomial constraint on the effective focal length. Based on this constraint, we propose three calibration algorithms under different conditions. The proposed algorithms need no conic fitting of line images, need no scene information, and the calibration accuracy is high. Experiments demonstrate the efficiency of the proposed algorithms.
- Central catadioptric camera /
- camera calibration /
- camera intrinsic parameter /
- sphere imaging model

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参考文献(1)

[1]

Yagi Y, Yachida M. Real-time omnidirectional image sensors. International Journal of Computer Vision, 2004, 58(3): 173-207[2] Swaminathan R, Grossberg M D, Nayar S. Non-single viewpoint catadioptric cameras: geometry and analysis. International Journal of Computer Vision, 2006, 66(3): 211-229[3] Baker S, Nayer S. A theory of single-viewpoint catadioptric image formation. International Journal of Computer Vision, 1999, 35(2): 175-196[4] Geyer C, Daniilidis K. Catadioptric projective geometry. International Journal of Computer Vision, 2001, 45(3): 223-243[5] Wu Y H, Hu Z Y. Geometric invariants and applications under catadioptric camera model. In: Proceedings of the 10th IEEE International Conference on Computer Vision. Beijing, China: IEEE, 2005. 1547-1554[6] Scaramuzza D, Martinelli A, Siegwart R. A flexible technique for accurate omnidirectional camera calibration and structure from motion. In: Proceedings of the IEEE International Conference on Computer Vision Systems. Washington D.C., USA: IEEE, 2006. 45-45[7] Gasparini S, Sturm P, Barreto J. Plane-based calibration of central catadioptric cameras. In: Proceedings of the 12th IEEE International Conference on Computer Vision. Kyoto, Japan: IEEE, 2009. 1195-1202[8] Sturm P, Ramalingam S. A generic concept for camera calibration. In: Proceedings of the European Conference on Computer Vision. Prague, Czech Republic: Springer, 2004. 1-13[9] Ramalingam S, Sturm P, Lodha S K. Towards generic self-calibration of central cameras. In: Proceedings of the 6th Workshop on Omnidirectional Vision Camera Networks and Nonclassical Cameras. Beijing, China: IEEE, 2005. 20-27[10] Kang S B. Catadioptric self-calibration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Hilton Head Island, USA: IEEE, 2000. 201-207[11] Barreto J P, Araujo H. Geometric properties of central catadioptric line images and their application in calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(8): 1327-1333[12] Geyer C, Daniilidis K. Catadioptric camera calibration. In: Proceedings of the IEEE International Conference on Computer Vision. Kerkyra, Greece: IEEE, 1999. 398-404[13] Geyer C, Daniilidis K. Para catadioptric camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(5): 687-695[14] Ying X H, Hu Z Y. Catadioptric camera calibration using geometric invariants, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(10): 1260-1271[15] Wu F C, Duan F Q, Hu Z Y, Wu Y H. A new linear algorithm for calibrating central catadioptric cameras. Pattern Recognition, 2008, 41(10): 3166-3172[16] Ying X H, Zha H B. Identical projective geometric properties of central catadioptric line images and sphere images with applications to calibration. International Journal of Computer Vision, 2008, 78(1): 89-105[17] Duan F Q, Wu F C, Hu Z Y. Pose determination and plane measurement using a trapezium. Pattern Recognition Letters, 2008, 29(3): 223-231[18] Deng Xiao-Ming, Wu Fu-Chao, Wu Yi-Hong. An easy calibration method for central catadioptric cameras. Acta Automatica Sinica, 2007, 33(8): 801-808[19] Barreto J P, Araujo H. Fitting conics to paracatadioptric projection of lines. Computer Vision and Image Understanding, 2006, 101(3): 151-165[20] Wu Y H, Li Y F, Hu Z Y, Easy calibration for para-catadioptric-like camera. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. Beijing, China: IEEE, 2006. 5719-5724[21] Ying X H, Zha H B. Simultaneously calibrating catadioptric camera and detecting line features using Hough transform. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. Edmonton, Canada: IEEE, 2005. 412-417[22] Sturm P, Cheng Z L, Chen P C Y, Poo A N. Focal length calibration from two views: method and analysis of singular cases. Computer Vision and Image Understanding, 2005, 99(1): 58-95[23] Fitzgibbon A, Pilu M, Fisher R B. Direct least square fitting of ellipses. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999, 21(5): 476-480

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