ru/ ru

ISSN: 1023-5086


ISSN: 1023-5086

Scientific and technical

Opticheskii Zhurnal

A full-text English translation of the journal is published by Optica Publishing Group under the title “Journal of Optical Technology”

Article submission Подать статью
Больше информации Back

DOI: 10.17586/1023-5086-2020-87-08-63-71

Evaluation of the quantization error in the convergence stereocameras

For Russian citation (Opticheskii Zhurnal):

Sayyedbarzani S.A., Emam S.M. Evaluation of the quantization error in the convergence stereocameras (Оценка ошибки дискретизации в конвергенционных стереокамерах) [на англ. яз.] // Оптический журнал. 2020. Т. 87. № 8. С. 63–71.


Sayyedbarzani S.A., Emam S.M. Evaluation of the quantization error in the convergence stereocameras (Оценка ошибки дискретизации в конвергенционных стереокамерах) [in English] // Opticheskii Zhurnal. 2020. V. 87. № 8. P. 63–71.


For citation (Journal of Optical Technology):

Computer vision techniques include the image acquisition, image processing and data analysis are currently popular and widespread. Stereo vision systems in the area of computer vision have been widely used in many applications, such as 3D depth reconstruction, 3D gaming, robot vision navigation, remote sensing, medical image processing, and so on. However, the high accurate 3D stereo vision scanners do not completely satisfy industry requirements due to the high price of these systems. In this paper, a novel method is proposed that increase the 3D measurement accuracy of stereo vision systems with low resolution and cheap cameras. This was achieved through discussing the quantization error of the 3D depth reconstruction of the stereo cameras. Accordingly, a mathematical model was introduced to evaluate the convergence stereo cameras quantization error. Meanwhile, a synthetic experiment was employed to validate our proposed mathematical model. Finally, the simulation experiments were conducted to demonstrate how to apply the presented approach to increase the accuracy measurement of the low resolution stereo vision cameras.
The simulation results showed a considerable measurement improvement by changing the convergence angle of the stereo vision cameras.


machine vision, stereo cameras, convergence angle, quantization error

OCIS codes: 150.0150, 150.3045, 110.3010, 100.3010, 100.6640


1. Luo P.F., Pan S.P., Lee C.L. Application of computer vision and laser interferometer to two-dimensional
inspection // Optical Engineering, 2008. V. 47(12). P. 123601.
2. Robla-Gómez S., Becerra V.M., Llata J.R., Gonzalez-Sarabia E., Torre-Ferrero C., Perez-Oria J. Working together: A review on safe human-robot collaboration in industrial environments // IEEE Access. 2017. V. 5. P. 26754–26773.
3. Aggarwal J.K., Xia L. Human activity recognition from 3D data: A review // Pattern Recognition Letters. 2014. V. 48. P. 70–80.
4. Ambrosch K., Kubinger W. Accurate hardware-based stereo vision // Computer Vision and Image Understanding. 2010. V. 114(11). P. 1303–1316.
5. Zhang J., Zhang P., Deng H., Wang J. High-accuracy three-dimensional reconstruction of vibration based on stereo vision // Optical Engineering. 2016. V. 55(9). P. 091410.
6. Zhang S. (Ed.) Handbook of 3D machine vision: Optical metrology and imaging. Boca Raton, London, New York: CRC Press, 2013. P. 1–31.
7. Jin S., Cho J., Dai Pham X., Lee K.M., Park S.K., Kim M., Jeon J.W. FPGA design and implementation of a real-time stereo vision system // IEEE transactions on circuits and systems for video technology. 2010. V. 20(1). P. 15–26.
8. Boehnen C., Flynn P. Accuracy of 3D scanning technologies in a face scanning scenario // Fifth International Conference on 3D Digital Imaging and Modeling (3DIM’05). 2005. P. 310–317.
9. Blostein S.D., Huang T.S. Error analysis in stereo determination of 3D point positions // IEEE Transactions on Pattern Analysis & Machine Intelligence. 1987. V. 6. P. 752–765.
10. Chen J., Khatibi S., Kulesza W. Depth reconstruction uncertainty analysis and improvement – The dithering approach // Image and Vision Computing. 2010. V. 28(9). P. 1377–1385.
11. Park J.H., Jung S., Choi H., Kim Y., Lee B. Depth extraction by use of a rectangular lens array and onedimensional elemental image modification // Applied optics. 2004. V. 43(25). P. 4882–4895.
12. Segall C.A., Molina R., Katsaggelos A.K. High-resolution images from low-resolution compressed video // IEEE Signal Processing Magazine. 2003. V. 20(3). P. 37–48.
13. Hardie R. A fast image super-resolution algorithm using an adaptive Wiener filter // IEEE Transactions on Image Processing. 2007. V. 16(12). P. 2953–2964.
14. Zitova B., Flusser J. Image registration methods: a survey // Image and vision computing. 2003. V. 21(11). P. 977–1000.
15. McCann M.T., Jin K.H., Unser M. Convolutional neural networks for inverse problems in imaging: A review // IEEE Signal Processing Magazine. 2017. V. 34(6). P. 85–95.
16. Bonchev S., Alexiev K. Improving super-resolution image reconstruction by in-plane camera rotation //
13th International Conference on Information Fusion. EICC Edinburgh, UK. 26–29 July 2010. P. 1–7.
17. Ben-Ezra M., Zomet A., Nayar S.K. Jitter camera: High resolution video from a low resolution detector // IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, USA.
27 Jun 2004. P. 135–142.
18. Lu Z., Tai Y.W., Deng F., Ben-Ezra M., Brown M.S. A 3D imaging framework based on high-resolution photometricstereo and low-resolution depth // International Journal of Computer Vision. 2013. V. 102 (1–3). P. 18–32.
19. Emam S.M., Khatibi S., Khalili K. Improving the accuracy of laser scanning for 3D model reconstruction using dithering technique // Procedia Technology. 2014. V. 12. P. 353–358.
20. Taryudi T., Wang M.S. Eye to hand calibration using ANFIS for stereo vision-based object manipulation
system // Microsystem Technologies. 2018. V. 24(1). P. 305–317.

21. Karami M., Mousavinia A., Ehsanian M. A general solution for Iso-disparity layers and correspondence
field model for stereo systems // IEEE Sensors Journal. 2017. V. 17(12). P. 3744–3753.
22. Mariottini G.L., Prattichizzo D. EGT for multiple view geometry and visual servoing: robotics vision with
pinhole and panoramic cameras // IEEE Robotics & Automation Magazine. 2005. V. 12(4). P. 26–39.
23. Samper D., Santolaria J., Aguilar J.J. MetroVisionLab Toolbox for Camera Calibration and Simulation.
24. Nguyen H., Wang Z., Jones P., Zhao B. 3D shape, deformation, and vibration measurements using infrared Kinect sensors and digital image correlation // Applied optics. 2017. V. 56(32). P. 9030–9037.
25. Sun J., Liu Q., Liu Z., Zhang G. A calibration method for stereo vision sensor with large FOV based
on 1D targets // Optics and Lasers in Engineering. 2011. V. 49(11). P. 1245–1250.
26. Stereo camera calibrator app (Matlab 2020),
27. Samper D., Santolaria J., Aguilar J.J. METROVISIONLAB Toolbox for Camera Calibration and Simulation. 2010.
28. Emam S.M., Sayyedbarzani S.A. Dimensional deviation measurement of ceramic tiles according to ISO 10545-2 using the machine vision // The International Journal of Advanced Manufacturing Technology.
2019. V. 100(5–8). P. 1405–1418.