Camera Self-calibration Using Multiple Geometric Constraints in a Single Image

  • Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210046, China

Received date: 2012-04-10

  Revised date: 2012-09-17

  Online published: 2012-10-25


Camera self-calibration is a key step to acquisition 3D space information from 2D image, and it is always one of the important issues in photogrammetry. However, present methods for camera self-calibration need two or more images and/or their corresponding points. With the development of digital devices for image taken and (wireless) network, a method not depending on digital device, images taken process, or multiple images, is badly needed. Consequently this paper presented a novel method that makes full use of various geometric constraints to realize reliable camera calibration for a single image. Firstly, this paper summarized various geometric constraints and invariants for the existing camera self-calibration method. Secondly, in order to build the relationships among geometric constraints for calibration, we coded for different planes and geometric features in an image. Because variance represents the error distribution, it can be considered as the determinant. In this paper, we obtained the variance of different combination of geometric features for camera calibration by means of fitting each groups of geometric features for thirty times, and then depended on the variance above to determine the weight of each camera's internal parameters. Finally, based on each camera's internal parameters, here we only focus on foci length, and their corresponding weights, the ultimate results are computed. Two images which depict inside and outside scene respectively were chosen to test the usability of our methods. In order to avoid the influence of image distortion, we corrected it using amethod we proposed in another paper before tests. The test results show that: 1) the weighted method gave a more stable result, relative to the result of each group geometric constraints, that is one group's relative error is two high and in other may be lower; 2) the weighted method obtained a higher accuracy result than the mean of all groups. The results of verification testing for the two images of the indicated that our weighted method can comprehensive employs variety of geometric constraints in single image, in the other side, it also takes their corresponding variance into account. It makes full use of the variety, usability and stability of geometric constraints. It can be employed to images depict indoor and outdoor which contains more geometric constraints.

Cite this article

WANG Mei-Zhen, LIU Hua-Jun-*, LEI Yue, LIU Dan . Camera Self-calibration Using Multiple Geometric Constraints in a Single Image[J]. Journal of Geo-information Science, 2012 , 14(5) : 644 -651 . DOI: 10.3724/SP.J.1047.2012.00644


[1] 孟晓桥,胡占义. 摄像机自标定方法的研究与进展[J]. 自动化学报,2003,29(1):110-124.

[2] Zhao Z, Liu Y and Zhang Z. Camera calibration with three non-collinearpoints under special motions[J]. IEEE Trans. Image Process,2008,17(12):2393-2402.

[3] Hong W, Yang Y A, Huang K and Ma Y. On symmetry and multiple-view geometry: Structure, pose, and calibration from a single image[J]. International Journal of Computer Vision,2004,60(3):241-265.

[4] Wu Y H, Li X J, Wu F C, Hu Z Y. Coplanar circles, quasi-affine invariance and calibration[J]. Image and Vision Computing,2006,24:319-326.

[5] Wang G, Tsui H T, Hu Z and Wu F. Camera calibration and 3Dreconstruction from a single view based on scene constraints[J]. Image and Vision Computing, 2005,23(3):311-323.

[6] 吴福朝,王光辉,胡占义. 由矩形确定摄像机内参数与位置的线性方法[J]. 软件学报,2003,14(3):703-712.

[7] 段福庆,吴福朝,胡占义. 基于平行性约束的摄像机标定与3D重构[J].软件学报,2007,18(6):1350-1360.

[8] 邱卫国,昂海松. 基于单张平行六面体照片的摄像机标定方法[J]. 传感器技术,2005,24(6):85-88.

[9] 魏峰,王小林. 采用单幅圆柱体图像的摄像机标定[J]. 工程图学学报,2009,(1):109-113.

[10] Miyagawa I, Arai H, Koike H. Simple camera calibration from a single image using five points on two orthogonal 1-D Objects[J]. IEEE Transactions on Image Processing, 2010,19(6):1528-1538.

[11] Chen Y S, Ip H, Huang Z J, et al. Full camera calibration from a single view of planar scene. //Bebis B et al. (Eds.). ISVC 2008, Part I, LNCS 5358, 2008:815-824.

[12] Kim J S, Gurdjos P and Kweon I S. Geometric and algebraic constraints of projected concentric circles and their applications to camera calibration[J]. IEEE PAMI, 2005, 27(4): 637-642.

[13] Zhong H, Mai F and Hung Y S. Camera calibration using circle and right angles . //Proc. of ICPR'06, 2006): 646-649.

[14] 谢文寒,张祖勋,张剑清. 一种新的基于灭点的相机标定方法[J]. 哈尔滨工业大学学报,2003,35(11):1384-1387,1391.

[15] Caprile B, Torre V. Using vanishing points for camera calibration[J]. International Journal of Computer Vision,1990,4(2):127-140.

[16] Meng X Q, Hu Z Y. A new easy camera calibration technique based on circular points[J]. Pattern Recognition, 2003,36:1155-1164.

[17] Hartley R, Zisserman A. Multiple view geometry in computer vision [M]. Cambridge, UK: Cambridge University Press, 2000.

[18] 卢玥,刘学军,王美珍,等. 基于数位的相机径向畸变参数计算[J]. 地理与地理信息科学,2011,27(6):18-22.