A new easy technique for calibrating a camera based on circular points is proposed. The proposed technique only requires the camera to observe a newly designed planar calibration pattern (referred to as the model plane hereinafter) which includes a circle and a pencil of lines passing through the circle抯 center, at a few (at least three) different unknown orientations, then all the five intrinsic parameters can be determined linearly. The main point of the proposed technique is that it does not need know metric measurement on the model plane and the corres pondences between points on the model plane and image one,hence it can be done fully automatically.The proposed technique is particularly use for those people who are not familiar with computer vision.Experiments with simulated datawell as well as with real images shoe that the new technique is robust and accurate.
[1] Brown, D.C. Close-range camera calibration. Photogrammetric Engineering, 1971,37(8):855~866.
[2] Faig, W. Calibration of close-range photogrammetry system: mathematical formulation. Photogrammetric Engineering and Remote Sensing, 1975,41(12):1479~1486.
[3] Zhang, Z. A flexible new technique for camera calibration, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000,22(11):1330~1334.
[4] Liebowitz, D., Zisserman, A. Metric rectification for perspective images of planes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Santa Barbara, CA:IEEE Computer Society, 1998. 482~488.
[5] 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. San Juan, Puerto Rico: IEEE Computer Society Press, 1997. 1100~1105.
[6] Zhang, Z. Motion and structure from two perspective views: from essential parameters to euclidean motion via fundamental matrix. Journal of the Optical Society of America A, 1997,14(11):2938~2950.
[7] Yang, C.J., Hu, Z.Y. Planar conic based camera calibration. In: Sanfeliu, A., Villanueve, J.J., Vanrell, M., et al., eds. Proceedings of the International Conference on Pattern Recognition. Barcelona: IEEE Computer Society Press, 2000. 555~558.
[9] Ma, Y., Soatto, S., Kosecka, J., et al. Euclidean reconstruction and reprojection up to subgroups. In: Proceedings of the International Conference on Computer Vision. Kerkyra: IEEE Computer Society Press, 1999. 219~229.
[10] Bookstein, F.L. Fitting conic sections to scattered data. Computer Graphics and Image Processing, 1979,9:56~71.
[11] Kanatani, K. Geometric Computation for Machine Vision. Oxford: Oxford Science Publications, 1993.
[12] Xu, L., Oja, E. Randomized hough transform(RHT): basic mechanism, algorithms, and computational complexities. CVGIP: Image Understanding, 1993,57(2):131~154.