A Water-Level Measurement Method Using Sparse Representation | Automatic Control and Computer Sciences Skip to main content
Springer Nature Link
Log in

A Water-Level Measurement Method Using Sparse Representation

  • Published:
Automatic Control and Computer Sciences Aims and scope Submit manuscript

Abstract

The water level measurement method based on image processing has entered a stage of rapid development in recent years due to its visibility and confirmability. However, the water level measurement method based on image processing is very susceptible to water stains, residual water level line, lighting conditions and other factors, and the measurement accuracy is difficult to compare with the traditional water level measurement method. This paper proposes a water level measurement method based on image processing and sparse representation. The experiment indicated that the method has a strong robustness to light variation, local disability, foreign matter occlusion, and so forth. Further, the maximum error of the method is less than 0.9 cm, which is significantly smaller than other image processing based water level recognition methods such as frame difference method, image segmentation method and Hough transform.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.
Fig. 9.
Fig. 10.
Fig. 11.
Fig. 12.
Fig. 13.
Fig. 14.
Fig. 15.
Fig. 16.
Fig. 17.

Similar content being viewed by others

REFERENCES

  1. Crabit, A., Colin, F., Bailly, J.S., Ayroles, H., and Garnier, F., Soft water level sensors for characterizing the hydrological behaviour of agricultural catchments, Sensors, 2011, vol. 11, no. 5, pp. 4656–4673.

    Article  Google Scholar 

  2. Kim, J., Han, Y., and Haahn, H., Image-based water level measurement method under stained ruler, J. Measur. Sci. Instrum., 2010, vol. 1, no. 1, pp. 28–31.

    Google Scholar 

  3. Zhu, H., New algorithm of liquid level of infusion bottle based on image processing, International Conference on Information Engineering and Computer Science. IEEE, Beijing, 2009, pp. 1–4.

  4. Thuy Tuong, N., Xuan Dai, P., and Jae Wook, J., An improvement of the standard Hough transform to detect line segments, 2008 IEEE International Conference on Industrial Technology, Chengdu, 2008, pp. 1–6.

  5. Aharon, M., Elad, M., and Bruckstein, A., K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation, IEEE Trans. Signal Process., 2006, vol. 54, no. 11, pp. 4311–4322.

    Article  Google Scholar 

  6. Sakurai-Amano, T., Nakasugi, K., and Takagi, M., Detection of seasonal water level changes in Amazon forests from JERS-1 SAR images using isolated strong scatterers, Proceedings of SPIE—The International Society for Optical Engineering, 2002, vol. 5, pp. 2140–2142.

  7. Feng, N. and Huang, H., Measurement error analysis of bubble water level meter, Water Resour. Inf., 2018.

    Google Scholar 

  8. Takagi, Y., Tsujikawa, A., and Takato, M., Development of a noncontact liquid level measuring system using image processing, Water Sci. Technol., 1998, vol. 37, no. 12, pp. 381–387.

    Article  Google Scholar 

  9. Kim, J., Han, Y., and Hahn H., Embedded implementation of image-based water-level measurement system, Comput. Vision IET, 2011, vol. 5, no. 2, pp. 125–133.

    Article  Google Scholar 

  10. Mallat, S.G. and Zhang, Z., Matching pursuits with time-frequency dictionaries, IEEE Trans. Signal Process., 1993, vol. 41, no. 12, pp. 3397–3415.

    Article  Google Scholar 

  11. Wright, J., Yang A.Y., and Ganesh, A., Face recognition with contiguous occlusion using Markov random fields, Computer Vision, 2009 IEEE 12th International Conference, Kyoto, 2009, pp. 1050–1057.

  12. Bao, P., Zhang, L., and Wu, X., Canny edge detection enhancement by scale multiplication, IEEE Trans. Pattern Anal. Mach. Intell., 2005, vol. 27, no. 9, pp. 1485–1490.

    Article  Google Scholar 

  13. Rahman, Z.U., Jobson, D.J., and Woodell, G.A., Retinex processing for automatic image enhancement, Human Vision and Electronic Imaging VII.International Society for Optics and Photonics, 2004, pp. 100–110.

    Google Scholar 

  14. Uji, A., Balasubramanian, S., and Lei, J., Impact of multiple en face image averaging on quantitative assessment from optical coherence tomography angiography images, Ophthalmology, 2017, vol. 124, no. 7, pp. 944–952.

    Article  Google Scholar 

  15. Illingworth, J. and Kittler, J., The adaptive Hough transform, IEEE Trans. Pattern Anal. Mach. Intell., 2009, vol. 9, no. 5, pp. 690–698.

    Google Scholar 

Download references

Funding

This work is partially supported by the Research Foundation of Education Bureau of Jilin Province (JJKN20190710KJ) and the Nation Natural Science Foundation of Beijing (9174047).

Author information

Authors and Affiliations

  1. Department of Information Engineering, Northeast Electric Power University, 132012, Jilin City, Jilin Province, P. R. China

    Shuqiang Guo, Yaoyao Zhang & Yu Liu

Authors
  1. Shuqiang Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yaoyao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yu Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shuqiang Guo.

Ethics declarations

ACKNOWLEDGMENTS

The authors also gratefully acknowledge the detailed comments and suggestions of the reviewers, which have helped to improve this manuscript.

CONFLICT OF INTERESTS

The authors declare that they have no conflicts of interest.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shuqiang Guo, Zhang, Y. & Liu, Y. A Water-Level Measurement Method Using Sparse Representation. Aut. Control Comp. Sci. 54, 302–312 (2020). https://doi.org/10.3103/S0146411620040069

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S0146411620040069

Keywords:

Search

Navigation