Autonomous Perceptual Projection Correction Technique of Deep Heterogeneous Surface

  • Fan Yang
  • Baoxing Bai
  • Cheng Han
  • Chao Zhang
  • Yuying Du
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10636)

Abstract

This paper proposes a projection correction method which to improve the adaptive perception projection of the projection equipment in different environments. Firstly, in the process of photon signal transmission, projector-camera can cause the loss of photon signal due to the coupling of system channel. Therefore, this paper proposes a system coupling correction scheme, which effectively reduces the system coupling crosstalk. Secondly, in order to establish the feature mapping relationship between the projection image and the deep heterogeneous surface quickly, a projection feature image of color structured light mesh fringe is designed. Finally, due to the feature point of the heterogeneous surface is quite different in topological structure, it will lead to the problem of inconsistent geometric mapping relation. For this reason, a projective geometric correction algorithm for topological analysis is proposed, analyzing the spatial topological distribution of the depth heterogeneous surface and solving the homography matrix of each region in the heterogeneous surface, then the geometric correction of the projected distortion image is solved by using the homography matrix set. From the experimental analysis we can see that, in the deep heterogeneous surface environment, the average error, the maximum error and the root-mean-square error of the correction image respectively are 0.424 pixels, 0.862 pixels and 0.216 pixels. At the same time, the parallelism of the distortion correction image is kept 90° substantially. It can be seen that the geometric distortion correction accuracy of this method has reached the sub-pixel level and the imaging screen consistency level.

Keywords

Computer vision Irregular surfaces Geometric correction Color structured light Depth perception 

Notes

Acknowledgement

This work was supported by the National Natural Science Foundation of China (Grant No. 61602058), Jilin province science and technology development plan item (20160101258JC), Jilin province science and technology development plan item (20150101015JC).

References

  1. 1.
    Majumder, A., Sajadi, B.: Large area displays: the changing face of visualization. Computer 46(5), 26–33 (2013)CrossRefGoogle Scholar
  2. 2.
    Park, J., Lee, B.U.: Defocus and geometric distortion correction for projected images on a curved surface. Appl. Optics 55(4), 1–25 (2016)CrossRefGoogle Scholar
  3. 3.
    Okatani, T., Deguchi, K.: Autocalibration of a projector-camera system. IEEE Trans. Patt. Anal. Mach. Intell. 27(12), 1845–1855 (2015)CrossRefGoogle Scholar
  4. 4.
    Zhu, B., Xie, L.J., Yang, T.J., Wang, Q.H., Zheng, Y.: An adaptive calibration algorithm for projected images in daily environment. J. Comput.-Aided Des. Comput. Graph. 24(7), 941–948 (2012)Google Scholar
  5. 5.
    Xiao, C., Yang, H.Y., Liang, H.J., Ji, Y.L., Li, X.S.: Geometric calibration for multi-projector display system based on structured light. J. Comput.-Aided Des. Comput. Graph. 25(6), 802–808 (2013)Google Scholar
  6. 6.
    Sun, W., Yang, X., Xiao, S., Hu, W.: Robust checkerboard recognition for efficient nonplanar geometry registration in projector-camera systems. In: Proceedings of the 5th ACM/IEEE International Workshop on Projector Camera Systems, pp. 504–542. ACM, New York (2008)Google Scholar
  7. 7.
    Xie C., Wang Q., Cheng W.: Simple auto-geometric correction for non-planar projection. In: International Conference on Automatic Control and Artificial Intelligence, pp. 1834–1837. IET, London (2012)Google Scholar
  8. 8.
    Steimle J., Jordt A., Maes P.: Flexpad: highly flexible bending interactions for projected handheld displays. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 237–246. ACM, New York (2013)Google Scholar
  9. 9.
    Madi, A., Ziou, D.: Color constancy for visual compensation of projector displayed image. Displays 35(1), 6–17 (2014)CrossRefGoogle Scholar
  10. 10.
    Xu, J., Wang, P., Yao, Y., Liu, S., Zhang, G.: 3D multi-directional sensor with pyramid mirror and structured light. Optics Lasers Eng. 93, 156–163 (2017)CrossRefGoogle Scholar
  11. 11.
    Boroomand, A., Sekkati, H., Lam, M., Clausi, D., Wong, A.: Saliency-guided projection geometric correction using a projector-camera system. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 2951–2955. IEEE (2016)Google Scholar
  12. 12.
    Fan, J.T., Han, C., Zhang, C., Li, M.X., Bai, B.X., Yang, H.M.: Study of a new decoding technology for de bruijn structured light. Acta Electronica Sinica 40(3), 483–488 (2012)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Fan Yang
    • 1
    • 2
  • Baoxing Bai
    • 1
    • 2
  • Cheng Han
    • 1
    • 2
  • Chao Zhang
    • 1
    • 2
  • Yuying Du
    • 1
    • 2
  1. 1.School of Computer Science and TechnologyChangchun University of Science and TechnologyChangchunChina
  2. 2.Special Film Technology and Equipment National Local Joint Engineering Research CenterChangchunChina

Personalised recommendations