© The Institution of Engineering and Technology
The precision of the camera calibration is one of the key factors that affect attitude measurement accuracy in many computer vision tasks. This study proposes a new calibration approach for binocular cameras. Firstly, based on singular value decomposition, the best transformation matrix to the essential matrix is approximated as the initial guess, which is solved in using the Frobenius norm. Secondly, the initial guess is refined through maximum likelihood estimation. A new calculating expression is derived for computing the relative position matrix of the binocular cameras. The Levenberg–Marquardt algorithm is then implemented to refine the initial guess. Large sets of synthesised and real point correspondences were tested to demonstrate the validity of the proposed method. Extensive experiments demonstrated that the proposed method outperforms the state-of-the-art methods. The error rate of the proposed method was 0.5% for the length test and about 1% for the angle test at a range of 1 m. This method can advance three-dimensional (3D) computer vision one additional step from laboratory environments to real-world use.
References
-
-
1)
-
3. Comlekciler, I.T., Gunes, S., Irgin, C.: ‘Three-Dimensional Repositioning of Jaw in the Orthognathic Surgery Using the Binocular Stereo Vision’, Scientia Iranica, 2017, 0, pp. 1–33.
-
2)
-
18. Bradski, G.: ‘OpenCV’, 2011. .
-
3)
-
12. Bergamasco, F., Cosmo, L., Albarelli, A., et al: ‘Camera calibration from coplanar circles’. Int. Conf. on Pattern Recognition, Stockholm, Sweden, August 2014, pp. 2137–2142.
-
4)
-
8. Heng, L., Lee, G.L., Pollefeys, M.: ‘Self-Calibration and Visual Slam with a Multi-Camera System on a Micro Aerial Vehicle’, Autonomous Robots, 2015, 39, pp. 259–277.
-
5)
-
16. Hartley, R.I.: ‘Estimation of relative camera positions for uncalibrated cameras’. European Conf. on Computer Vision, Santa Margherita Ligure, Italy, May 1992.
-
6)
-
21. Richard, H.: ‘In defence of the 8-point algorithm’. Fifth Int. Conf. on Computer Vision, Proc., Cambridge, MA, USA, June 1995, pp. 1064–1070.
-
7)
-
4. Nie, Y., Song, Z.: ‘A novel photometric stereo method with noniso-tropic point light sources’. 23rd Int. Conf. on Patern Recognition (ICPR), Cancún, México, December 2016, pp. 1737–1742.
-
8)
-
10. Lei, T., Yaonan, W., Hongshan, Y., et al: ‘Automatic camera calibration using active displays of a virtual pattern’, Sensors, 2017, 17, (4), p. 685.
-
9)
-
6. Long, C., Guo, B., Wei, S.: ‘Relative pose measurement algorithm of non-cooperative target based on stereo vision and ransac’, Int. J. Soft Comput. Softw. Eng., 2012, 2, (4), pp. 26–35.
-
10)
-
23. Faugeras, O.: ‘Three-dimensional computer vision, a geometric viewpoint’ (MIT Press, USA, 1993).
-
11)
-
9. Zhengyou, Z.: ‘A flexible new technique for camera calibration’, IEEE Trans. Pattern Anal. Mach. Intell., 2000, 22, (11), pp. 1330–1334.
-
12)
-
22. Atkinson, K.E.: ‘An Introduction to numerical analysis’ (John Wiley and Sons, New York, 1989, 2nd Edn.).
-
13)
-
2. Çalışkan, A., Çevik, U.: ‘Three-dimensional modeling in medical image processing by using fractal geometry’, J. of Comput., 2017, 12, pp. 479–485.
-
14)
-
5. Saleem, N.H., Klette, R.: ‘Accuracy of free-space detection: monocular versus binocular vision’. Proc. of 2016 Int. Conf. on Image and Vision Computing, New Zealand, Palmerston North, New Zealand, 2016, pp. 1–6.
-
15)
-
15. Longuet-Higgins, H.C.: ‘A computer algorithm for reconstructing a scene from two projections’, 1987, pp. 61–62.
-
16)
-
19. Scaramuzza, D., Harati, A., Siegwart, R.: ‘Extrinsic self calibration of a camera and a 3d laser range finder from natural scenes’. Int. Conf. on Intelligent Robots and Systems, San Diego, CA, USA, October 2007, pp. 5695–5701.
-
17)
-
7. Maybank, S.J., Faugeras, O.D.: ‘A theory of self-calibration of a moving camera’, Int. J. Comput. Vis., 1992, 8, (2), pp. 123–151.
-
18)
-
13. Agrawal, M., Davis, L.S.: ‘Camera calibration using spheres: A semi-definite programming approach’. IEEE Int. Conf. on Computer Vision, Nice, France, October 2003, pp. 1–8.
-
19)
-
1. Woodham, R.J.: ‘Photometric method for determining surface orientation from multiple images’, Opt. Eng., 1980, 19, (1), pp. 1–22.
-
20)
-
11. Oyamada, Y.: ‘Single camera calibration using partially visible calibration objects based on random dots marker tracking algorithm’. In Proc. of the IEEE ISMAR 2012 Workshop on Tracking Methods and Applications (TMA), Atlanta, GA, USA, 5–8 November 2012.
-
21)
-
14. Radu, O., Joaquim, S., Mihaela, G., et al: ‘Camera calibration using two or three vanishing points’. In Proc. of the 2012 Federated Conf. on Computer Science and Information Systems (FedCSIS), Wroclaw, Poland, 2012, pp. 123–130.
-
22)
-
17. Bouguet, J.Y.: ‘Camera calibration toolbox for matlab’, 2010. .
-
23)
-
20. Moré, J.J.: ‘The Levenberg-Marquardt algorithm: implementation and theory’, Lect. Notes Math., 1978, 630, pp. 105–116.
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