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Knowledge-based automatic extraction of multi-structured light stripes

  • Chao DingEmail author
  • Liwei Tang
  • Lijun Cao
  • Xinjie Shao
  • Wei Wang
  • Shijie Deng
Original Research Paper
  • 20 Downloads

Abstract

To achieve automatic processing of the multi-structured light stripe images, we need to extract many stripes automatically in a short time. However, due to the complexity and diversity of its work, the related research progress is very slow. Therefore, we first use the Radon transformation and grayscale transform enhancement to eliminate noise in images. Then, a new and adaptive feature model is created based on the knowledge of stripes distribution. The distribution of the stripes is matched by the model, and then the target stripe regions are picked up to realize the extraction of the stripes. In the actual verification, the qualified rate of the detection is basically over 80%, and the detection time is controlled at about 2 s. The automatic extraction of target stripes effectively avoids the tedious work that workers need to do it manually, and it is of great significance for the application of multi-structured light detection technology.

Keywords

Automatic extraction Manual contouring Multi-structured light stripes Knowledge model 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation Program of China (no. 51575523). I am deeply indebted to a number of people. The thesis would not have been completed without their assistance and encourage. I am profoundly grateful to my tutor, Professor Tang, whose instruction and advice have guided me through each step of my writing. My great gratitude also goes to some of my teachers and friends who are selfless and generously helped me with my thesis. They are Professor Cao, Professor Shao, Dr. Deng, Professor Wang, Teacher Su, etc.

Compliance with ethical standards

Funding

This study was funded by the National Natural Science Foundation Program of China (Grant number no. 51575523).

Conflict of interest

Author Chao Ding declares that he has no conflict of interest. Author Liwei Tang declares that he has no conflict of interest. Author Lijun Cao declares that he has no conflict of interest. Author Xinjie Shao declares that he has no conflict of interest. Author Wei Wang declares that he has no conflict of interest. Author Shijie Deng declares that he has no conflict of interest. We declare that we do not have any commercial interest that represents a conflict of interest in connection with the work submitted.

References

  1. 1.
    Dhillon, D.S., Govindu, V.M.: Geometric and radiometric estimation in a structured-light 3D scanner. Mach. Vis. Appl. 26, 339–352 (2015).  https://doi.org/10.1007/s00138-015-0667-0 CrossRefGoogle Scholar
  2. 2.
    Liu, J.Y., Liu, J.B., Guo, Z.H., Ren, D.C., Ren, Z.B., Li, Y.: A three-dimensional tool measurement system based on surface structured light. J. Electron. Meas. Instrum. 30, 1884–1891 (2016).  https://doi.org/10.13382/j.jemi.2016.12.011 Google Scholar
  3. 3.
    Ding, C., Tang, L.W., Cao, L.J., Shao, X.J., Deng, S.J.: Image distortion correction algorithm for complicated deep-hole profile using structured-light. Infrared Laser Eng. 46, 1217008 (2017).  https://doi.org/10.3788/IRLA201746.1217008 CrossRefGoogle Scholar
  4. 4.
    Pribanic, T., Diez, Y., Roure, F., Salvi, J.: An efficient surface registration using smartphone. Mach. Vis. Appl. 27, 559–576 (2016).  https://doi.org/10.1007/s00138-016-0751-0 CrossRefGoogle Scholar
  5. 5.
    Zheng, L.B., Wang, X.D., Yan, F.: 3D reconstruction method based on linear-structured light stripe for welding seam. Laser Optoelectron. Progr. 51, 041005 (2014).  https://doi.org/10.3788/LOP51.041005 CrossRefGoogle Scholar
  6. 6.
    Li, M.H., Bai, M., Lv, Y.J.: Adaptive thresholding based edge detection approach for images. Pattern Recognit. Artif. Intell. 29, 177–184 (2016).  https://doi.org/10.16451/j.cnki.issn1003-6059.201602010 Google Scholar
  7. 7.
    Mei, T.C., Zhong, S.D., He, D.Y.: Structured light stripe detection under variable ambient light. Chin. J. Sci. Instrum. 32, 2794–2801 (2011)Google Scholar
  8. 8.
    Cai, H.Y., Feng, Z.D., Huang, Z.H.: Centerline extraction of structured light stripe based on principal component analysis. Chin. J. Lasers 42, 0308006 (2015).  https://doi.org/10.3788/CJL201542.0308006 CrossRefGoogle Scholar
  9. 9.
    Barone, S., Paoli, A., Razionale, A.V.: Shape measurement by a multi-view methodology based on the remote tracking of a 3D optical scanner. Opt. Lasers Eng. 50, 380–390 (2012).  https://doi.org/10.1016/j.optlaseng.2011.10.019 CrossRefGoogle Scholar
  10. 10.
    Li, Y.F.: A sparsity regularized multiregion image segmentation method based on image decomposition. Acta Electronica Sinica 43, 1841–1849 (2015).  https://doi.org/10.3969/j.issn.0372-2112.2015.09.024 Google Scholar
  11. 11.
    Wang, Y., Wang, H., Kang, Y.: Knowledge-based automatic extraction method of spinal cord in CT images. Chin. J. Sci. Instrum. 34, 1367–1373 (2013)Google Scholar
  12. 12.
    Chen, S., Yang, J., Song, X.Q.: Analysis and automatic extraction of linear features in synthetic aperture radar images. Syst. Eng. Electron. 32, 1868–1874 (2010).  https://doi.org/10.3969/j.issn.1001-506X.2010.09.18 Google Scholar
  13. 13.
    Archip, N., Erard, P.J., Egmont-Petersen, M., Haefliger, J., Germond, J.: A knowledge-based approach to automatic detection of the spinal cord in CT images. IEEE Trans. Med. Imaging 21, 1504–1516 (2002)CrossRefGoogle Scholar
  14. 14.
    Shi, Y.H., Yu, Y.F., Cheng, X.H.: Mathematics in computerized tomography radon transform. J. Cap. Norm. Univ. (Natural Science Edition) 34, 15–18 (2013)Google Scholar
  15. 15.
    Liu, X.W., Chen, X.M., Liu, C.Y.: Image processing in welding seam tracking with structure light based on Radon transform and fuzzy-enhancement. Trans. China Weld. Inst. 38, 19–22 (2017)Google Scholar
  16. 16.
    Usamentiaga, R., Molleda, J., Garcia, D.F.: Fast and robust laser stripe extraction for 3D reconstruction in industrial environments. Mach. Vis. Appl. 23, 179–196 (2012).  https://doi.org/10.1007/s00138-010-0288-6 CrossRefGoogle Scholar
  17. 17.
    Ding, C., Tang, L.W., Cao, L.J., Shao, X.J., Deng, S.J.: Height difference detection of barrel rifling based on structured light. Opt. Precis. Eng. 25, 545–553 (2017).  https://doi.org/10.3788/OPE.20172504.1077 Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Shijiazhuang Mechanical Engineering CollegeShijiazhuangChina

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