A New Calibration Method for Binocular Camera Based on Fundamental Matrix & HEIV Model

A New Calibration Method for Binocular Camera Based on Fundamental Matrix & HEIV Model

Hui Ma 

Jilin Agricultural Science and Technology University, Jilin 132109, China

Corresponding Author Email: 
mahuiaini8@163.com
Page: 
333-345
|
DOI: 
https://doi.org/10.18280/ama_a.540215
Received: 
25 May 2017
|
Accepted: 
5 June 2017
|
Published: 
30 June 2017
| Citation

OPEN ACCESS

Abstract: 

With the fast development of geometrics’ industry, the implementation of digital city and digital earth strategy has become an important step for information construction. The 3D spatial data are the foundation and precondition for establishing digital earth and intelligent city. The binocular vision technology mainly comprising the steps of image acquisition, stereo matching, and 3D reconstruction can obtain geographic information quickly. Binocular camera calibration is an essential step to obtain 3D information from images. In this paper, a new 1D calibration method was proposed based on fundamental matrix. In this calibration method, 1D Object can move freely without any limitations, such as the space or prior information of the camera. Then a HEIV (Heteroscedastic Error-in-Variables) model was proposed to improve the calibration accuracy. Both simulation experiment and real image experiment were conducted to validate the feasibility and effectiveness of this algorithm.

Keywords: 

Geographic information, Binocular camera calibration, Fundamental matrix, HEIV model.

1. Introduction
2. Camera Geometric Model and HEIV Model
3. Fundamental Matrix Algorithm
4. HEIV-Based 1D Calibration Algorithm
5. Experimental Results
6. Conclusion
  References

1. R. Tsai, An efficient and accurate camera calibration technique for 3d machine vision, 1986, ROC IEEE Conf. on Computer Vision & Pattern Recognition, pp. 364–374.

2. K.F. Shi, F.C. Wu, Optimally weighted linear algorithm for camera calibration with 1D object, 2014, Journal of Computer-Aided Design &Computer Graphics, vol. 26, no. 8, pp. 1251-1257.

3. Z.Y. Zhang, Camera calibration with one- dimensional objects, 2004, European Conference on Computer Vision, Vol. 26, no. 7, pp. 892-899.

4. W.W. Wang, W. Yang, J. Luo, A new calibration method of camera external parameters, 2014, Semiconductor optoelectronics, vol. 35, no. 6, pp. 1127-1130.

5. X. Armangue, J. Salvi, Overall view regarding fundamental matrix estimation, 2003, Image and Vision Computing, vol. 21, no. 2, pp. 205–220.

6. O.D. Faugeras, What can be seen in three dimensions with an uncalibrated stereo rig, 1992, European Conference on Computer Vision, pp. 563–578.

7. R. Hartley, A. Zisserman, Multiple View Geometry in Computer Vision, 2000, Cambridge University Press.

8. M. Pollefeys, L.V. Gool, A Oosterlinck.The modulus constraint:a new constraint for self-calibration, 1996, Proc of the ICPR’96 Vienna Austria, pp. 31-42.

9.    J. Franca, M.R. Stemmer, Revisiting zhang’s 1d calibration algorithm, Pattern Recognition, vol. 43, no. 3, pp. 1180–1187.

10. Q. Fu, Q. Quan, K.Y. Cai, Calibration method and experiments of multi-camera’s parameters based on freely moving one-dimensional calibration object, 2014, Control Theory & Applications, vol. 31, no. 8, pp. 1018-1024.

11. G.H. Golub, C.F. Van Loan, Matrix Computations, 1996, The Johns University Press.

12. R. Horaud, G. Csurka, D. Demirdijian, Stereo calibration from rigid motions, 2000, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 12, pp. 1446– 1452.

13. L. Li, Camera calibration algorithm based on OpenCV and improved Zhang Zhengyou Algorithm, 2015, Light Industry Machinery, vol. 33, no. 4, pp. 60-68.

14. B.C. Matei, P. Meer, Estimation of nonlinear errors-in-variables models for computer vision application, 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence, Pattern Recognition, vol. 28, no. 10, pp. 1537−1552.

15. I. Miyagawa, H. Arai, H. Koike, Simple camera calibration from a single image using five points on two orthogonal 1-D objects, 2010, IEEE Trans Image Processing, vol. 19, no 6, pp. 1528-1538.

16. L. Wang, F.C. Wu, Z.Y. Hu, Multi-camera calibration with one-dimensional object under general motions, 2007, Proc of the ICCV’ 07 IEEE, pp. 1-7.

17. Z.Y. Zhang, A flexible new technique for camera calibration, 2000, IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 22, no. 11, pp. 1330–1334.

18. Z.J. Zhao, Y.C. Liu, Z.Y. Zhang, Camera calibration with three noncollinear points under special motions, 2008, IEEE Trans. Image Processing, Vol. 17, no. 12, pp. 2393-2402.

19. D. Zhou, L.Q. Zhou, J. Sun, A novel feedback error-correcting algorithm for automatic recognition of the color and weave pattern of yarn-dyed fabrics, 2015, Textile Research Journal, Vol. 83 no. 16, pp. 1673-1689.