3-D Dimension Measurement of Workpiece Based on Binocular Vision

  • Jiannan Wang
  • Hongbin MaEmail author
  • Baokui Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11741)


In this paper, the three-dimensional measurement of workpiece is studied, and the left and right images of the target workpiece are captured by binocular camera. Firstly, the calibration principle and theoretical model of binocular camera are studied in detail, and the calibration of inside and outside parameters of left and right cameras is completed under the environment of Matlab. Secondly, Hough transform is used to detect the contour of the target workpiece after image pre-processing such as filtering, graying and binarization, and to extract its two-dimensional feature point information. Then, on the basis of epipolar rectification, there is only lateral parallax between left and right images, and Block Matching (BM) algorithm is used for stereo matching of relative images. After that, the depth information of the pixels is extracted from the disparity map obtained by stereo matching, and the dimension of the workpiece is measured by combining the two-dimensional feature point information extracted by Hough transform and the three-dimensional Euclidean distance calculation formula. Finally, the experimental results show the validity of the proposed three-dimensional measurement results based on binocular vision.


Binocular vision Camera calibration Stereo matching Three-dimensional measurement 



This work is partially supported by National Key Research and Development Program of China under Grant 2017YFF0205306, National Nature Science Foundation of China under Grant 91648117, and Beijing Natural Science Foundation under Grant 4172055.


  1. 1.
    Mantri, S., Bullock, D.: Analysis of feedforward-backpropagation neural networks used in vehicle detection. Transp. Res. Part C 3(3), 161–174 (1995)CrossRefGoogle Scholar
  2. 2.
    Okada, K., Inaba, M., Inoue, H.: Integration of real-time binocular stereo vision and whole body information for dynamic walking navigation of humanoid robot. In: IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (2003)Google Scholar
  3. 3.
    Vallerand, S., Kanbara, M., Yokoya, N.: Binocular vision-based augmented reality system with an increased registration depth using dynamic correction of feature positions. In: Proceedings of the 2003 IEEE, Virtual Reality, pp. 271–272, 22–26 March 2003Google Scholar
  4. 4.
    Asada, M., Tanaka, T., Hosoda, K.: Visual tracking of unknown moving object by adaptive binocular visual servoing. In: Proceedings of the 1999 IEEE International Conference on Multisensor Fusion and Intelligent Systems (1999)Google Scholar
  5. 5.
    Olson, C.F., Abi-Rachedi, H., Ye, M., Hendrich, J.P.: Wide-baseline stereo vision for Mars rovers. In: Proceedings of the 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems, October 2003Google Scholar
  6. 6.
    Abdelaziz, Y.I.: Photogrammetric potential of non-metric cameras. Thesis Illinois Univ. 74, 134 (2002)Google Scholar
  7. 7.
    Pollefeys, M., Koch, R., Gool, L.V.: Self-calibration and metric reconstruction inspite of varying and unknown intrinsic camera parameters. Int. J. Comput. Vision 32(1), 7–25 (1999)CrossRefGoogle Scholar
  8. 8.
    Sturm, P., Triggs, B.: A factorization based algorithm for multi-image projective structure and motion. In: Buxton, B., Cipolla, R. (eds.) ECCV 1996. LNCS, vol. 1065, pp. 709–720. Springer, Heidelberg (1996). Scholar
  9. 9.
    Hartley, R.: Lines and points in three views and the trifocal tensor. Int. J. Comput. Vision 22(2), 125–140 (1997)CrossRefGoogle Scholar
  10. 10.
    Zhu, Z., Wang, X., Liu, Q., Zhang, F.: Camera calibration method based on optimal polarization angle. Opt. Lasers Eng. 112(SI), 128–135 (2019)CrossRefGoogle Scholar
  11. 11.
    Tang, H., Ni, R., Zhao, Y., Li, X.: Median filtering detection of small-size image based on CNN. J. Vis. Commun. Image Represent. 51, 162–168 (2018)CrossRefGoogle Scholar
  12. 12.
    Zhang, X., Wang, X.: Novel survey on the color-image graying algorithm. In: IEEE International Conference on Computer and Information Technology, Helsinki, Finland, pp. 750–753, August 2017Google Scholar
  13. 13.
    Tung, C.H., Wu, Z.L.: Binarization of uneven-lighting image by maximizing boundary connectivity. J. Stat. Manag. Syst. 20(2), 175–196 (2017)CrossRefGoogle Scholar
  14. 14.
    Bachiller-Burgos, P., Manso, L.J., Bustos, P.: A variant of the Hough transform for the combined detection of corners, segments, and polylines. EURASIP J. Image Video Process. (1), 32 (2017) Google Scholar
  15. 15.
    Lin, G.-Y., Xu, C., Zhang, W.-G.: A robust epipolar rectification method of stereo pairs. In: 2010 International Conference on Measuring Technology and Mechatronics Automation, Changsha, China, pp. 322–326, March 2010Google Scholar
  16. 16.
    Liu, T., Shi, J.: Study on improvement of BM algorithm for intrusion detection. In: IEEE International Conference on Signal and Image Processing (2017)Google Scholar
  17. 17.
    Hu, T., Huang, M.: A new stereo matching algorithm for binocular vision. In: International Conference on Hybrid Information Technology, pp. 42–44 (2009)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of AutomationBeijing Institute of TechnologyBeijingPeople’s Republic of China

Personalised recommendations