A Method of Planar-Template- Based Camera Self-Calibration for Single-View
Camera calibration is an important step in 3D
reconstruction. Camera calibration may be classified into two major types: traditional calibration and self-calibration. However, a calibration method in using a checkerboard is intermediate between traditional calibration and self-calibration. A self
is proposed based on a square in this paper. Only a square in the planar
template, the camera self-calibration can be completed through the single view. The proposed algorithm is that the virtual circle and straight line are established by a square on planar template, and
circular points, vanishing points in straight lines and the relation
between them are be used, in order to obtain the image of the absolute
conic (IAC) and establish the camera intrinsic parameters. To make
the calibration template is simpler, as compared with the Zhang Zhengyou-s method. Through real experiments and experiments, the experimental results show that this algorithm is
feasible and available, and has a certain precision and robustness.
[1] Hartley R., "Estimation of relative camera positions for uncalibrated cameras", In Proceedings of the European Conference on Computer
Vision, pp. 579-387, 1992.
[2] Maybank S.J., Faugeras O.D., "A theory of self-calibration of a moving
camera", International Journal of Computer Vision, vol. 8, pp. 123-151,Feb. 1992.
[3] Hartley R., "Self-calibration of stationary cameras", International Journal of Computer Vision, vol. 22, pp. 5-23, Jan. 1997.
[4] Dron L., "Dynamic camera self-calibration of from controlled motion
sequences", In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 501-506, 1993.
[5] Du F., Brady M., "Self-Calibration of the intrinsic parameters of cameras
for active vision systems", In Proceedings of the IEEE Conference on
Computer Vision and Pattern Recognition, pp. 477-482, 1993.
[6] Pollefeys M., Koch R., Gool L.V., "Self-Calibration and metric reconstruction in spite of varying and unknown internal camera parameters", In Proceedings of the International Conference on
Computer Vision, Bombay, pp. 90-95, 1998.
[7] Sturm P., "Critical motion sequences for monocular self-calibration and
uncalibrated Euclidean reconstruction", In Proceedings of the IEEE
Conference on Computer Vision and Pattern Recognition,
1100-1105(1997).
[8] Zhang Z., "A flexible new technique for camera calibration", IEEE
Transactions on Pattern Analysis and Machine Intelligence, vol. 22, pp.
1330-1334, Nov. 2000.
[9] Meng X.Q., Li H., Hu Z.Y., "A new easy camera calibration technique
based on circular points", Journal of Software, vol. 13, No. 5 pp.
957-965, May. 2002.
[10] Wu F.C.,Wang G.H.,Hu Z.Y., "A linear approach for determining
intrinsic parameters and pose of cameras from rectangles". Journal of
software, vol. 14, pp. 703-712, Feb. 2003.
[11] Xinju Li, Haijiang Zhu, Fuchao Wu, "Camera calibration based on
coplanar similar geometrical entities". PR&AI. vol. 17, pp. 457-461, Apr.
2004.
[12] Guanghui Wang, Q.M. Jonathan Wu, Wei Zhang, "Kruppa equation
based camera calibration from homography induced by remote plane",
Pattern Recognition Letters, vol. 29, pp. 2137-2144, Aug. 2008.
[13] Hui Wang, Yue Zhao, "A new planar circle-based approach for camera
self-calibration", Journal of Computational Information Systems, vol. 6,
pp, 2877-2883, Sep. 2010.
[14] Yue Zhao, Hui Wang, "Conic and circular points based camera linear
calibration", Journal of Information & Computational Science, vol. 7, pp.
2478-2485, Dec. 2010.
[15] Yue Zhao, Hui Wang, Xiaofu Wang, "A Conic-Based Approach for
Camera Linear Self-calibration", Journal of Information &
Computational Science, vol. 7, pp. 1959-1966, Sep. 2010.
[16] Hui Wang, Yue Zhao, Jianping Li, "Innovation experiment based on
circular points and Laguerre theorem in computer vision", In Proceedings
of 2nd International Conference on Education Technology and
Computer, pp. 25-28, 2010.
[17] Harris C.G., Stephens M. J., "A combined corner and edge detector", In
Proceedings of Fourth Alvey Vision Conference, pp. 147-151, 2001.
[18] Yunyoung Nam, Junghun Ryu, "Learning spatio-temporal topology of a
multi-camera network by tracking multiple people", Proceedings of
World Academy of Science, Engineering And Technology. vol. 24, pp.
175-180, Oct. 2007.
[19] Fuchao Wu, Mathematical methods in computer vision, Beijing: Science
Press, 2008.
[20] Hartley R., Zisserman A., Multiple view geometry in computer vision,
Cambridge: University Press, 2000.
[1] Hartley R., "Estimation of relative camera positions for uncalibrated cameras", In Proceedings of the European Conference on Computer
Vision, pp. 579-387, 1992.
[2] Maybank S.J., Faugeras O.D., "A theory of self-calibration of a moving
camera", International Journal of Computer Vision, vol. 8, pp. 123-151,Feb. 1992.
[3] Hartley R., "Self-calibration of stationary cameras", International Journal of Computer Vision, vol. 22, pp. 5-23, Jan. 1997.
[4] Dron L., "Dynamic camera self-calibration of from controlled motion
sequences", In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 501-506, 1993.
[5] Du F., Brady M., "Self-Calibration of the intrinsic parameters of cameras
for active vision systems", In Proceedings of the IEEE Conference on
Computer Vision and Pattern Recognition, pp. 477-482, 1993.
[6] Pollefeys M., Koch R., Gool L.V., "Self-Calibration and metric reconstruction in spite of varying and unknown internal camera parameters", In Proceedings of the International Conference on
Computer Vision, Bombay, pp. 90-95, 1998.
[7] Sturm P., "Critical motion sequences for monocular self-calibration and
uncalibrated Euclidean reconstruction", In Proceedings of the IEEE
Conference on Computer Vision and Pattern Recognition,
1100-1105(1997).
[8] Zhang Z., "A flexible new technique for camera calibration", IEEE
Transactions on Pattern Analysis and Machine Intelligence, vol. 22, pp.
1330-1334, Nov. 2000.
[9] Meng X.Q., Li H., Hu Z.Y., "A new easy camera calibration technique
based on circular points", Journal of Software, vol. 13, No. 5 pp.
957-965, May. 2002.
[10] Wu F.C.,Wang G.H.,Hu Z.Y., "A linear approach for determining
intrinsic parameters and pose of cameras from rectangles". Journal of
software, vol. 14, pp. 703-712, Feb. 2003.
[11] Xinju Li, Haijiang Zhu, Fuchao Wu, "Camera calibration based on
coplanar similar geometrical entities". PR&AI. vol. 17, pp. 457-461, Apr.
2004.
[12] Guanghui Wang, Q.M. Jonathan Wu, Wei Zhang, "Kruppa equation
based camera calibration from homography induced by remote plane",
Pattern Recognition Letters, vol. 29, pp. 2137-2144, Aug. 2008.
[13] Hui Wang, Yue Zhao, "A new planar circle-based approach for camera
self-calibration", Journal of Computational Information Systems, vol. 6,
pp, 2877-2883, Sep. 2010.
[14] Yue Zhao, Hui Wang, "Conic and circular points based camera linear
calibration", Journal of Information & Computational Science, vol. 7, pp.
2478-2485, Dec. 2010.
[15] Yue Zhao, Hui Wang, Xiaofu Wang, "A Conic-Based Approach for
Camera Linear Self-calibration", Journal of Information &
Computational Science, vol. 7, pp. 1959-1966, Sep. 2010.
[16] Hui Wang, Yue Zhao, Jianping Li, "Innovation experiment based on
circular points and Laguerre theorem in computer vision", In Proceedings
of 2nd International Conference on Education Technology and
Computer, pp. 25-28, 2010.
[17] Harris C.G., Stephens M. J., "A combined corner and edge detector", In
Proceedings of Fourth Alvey Vision Conference, pp. 147-151, 2001.
[18] Yunyoung Nam, Junghun Ryu, "Learning spatio-temporal topology of a
multi-camera network by tracking multiple people", Proceedings of
World Academy of Science, Engineering And Technology. vol. 24, pp.
175-180, Oct. 2007.
[19] Fuchao Wu, Mathematical methods in computer vision, Beijing: Science
Press, 2008.
[20] Hartley R., Zisserman A., Multiple view geometry in computer vision,
Cambridge: University Press, 2000.
@article{"International Journal of Engineering, Mathematical and Physical Sciences:62903", author = "Yue Zhao and Chao Li", title = "A Method of Planar-Template- Based Camera Self-Calibration for Single-View", abstract = "Camera calibration is an important step in 3D
reconstruction. Camera calibration may be classified into two major types: traditional calibration and self-calibration. However, a calibration method in using a checkerboard is intermediate between traditional calibration and self-calibration. A self
is proposed based on a square in this paper. Only a square in the planar
template, the camera self-calibration can be completed through the single view. The proposed algorithm is that the virtual circle and straight line are established by a square on planar template, and
circular points, vanishing points in straight lines and the relation
between them are be used, in order to obtain the image of the absolute
conic (IAC) and establish the camera intrinsic parameters. To make
the calibration template is simpler, as compared with the Zhang Zhengyou-s method. Through real experiments and experiments, the experimental results show that this algorithm is
feasible and available, and has a certain precision and robustness. ", keywords = "Absolute conic, camera calibration, circle point, vanishing point.", volume = "5", number = "12", pages = "2105-7", }
