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Apply Image Technology to River Level Estimation

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Genetic and Evolutionary Computing (ICGEC 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 536))

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Abstract

In this paper, an image based approach of the water flow information measurement is proposed. Applying the image based measurement is safely and efficiently non-contact method. This paper proposes the multiple virtual water level probes (MVWLP) method which can apply in any river environment without ruler where has regular water line on the embankment. This approach mainly applies the color space adjustable technique to reduce noises and uses the adaptive edge detection to extract the water line. Then, it sets some virtual probes on the discovered water line comparing with the preset probes to measure the current water level. We convince that the proposed methods are accurate, robust and adaptable enough to overcome multiple conditions presented in the sites.

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References

  1. Jodeaua, M., Hauetb, A., Paquiera, A., Le Coza, J., Dramaisa, G.: Application and evaluation of LS-PIV technique for the monitoring of river surface velocities in high flow conditions. Flow Meas. Instrum. 19(2), 117–127 (2008)

    Article  Google Scholar 

  2. Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-Projective-As-Possible image stitching with moving DLT. IEEE Trans. Pattern Anal. Mach. Intell. 36(7) (2014)

    Google Scholar 

  3. Dabiri, D.: Cross-correlation digital particle image velocimetry – a review. Exp. Fluids 46(2), 191–241 (2009)

    Article  Google Scholar 

  4. Gonzalez, R.C., Woods, R.E., Eddins, S.L.: Digital Image Processing Using MATLAB, 2nd edn. Gatesmark Publishing, Upper Saddle River (2009)

    Google Scholar 

  5. Keane, R.D., Adrian, R.J.: Theory of cross-correlation analysis of PIV images. Appl. Sci. Res. 49(3), 191–215 (1992)

    Article  Google Scholar 

  6. Keane, R.D., Adrian, R.J., Zhang, Y.: Super-resolution particle image velocimetry. Meas. Sci. Technol. 6, 754–768 (1995)

    Article  Google Scholar 

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Acknowledgments

This work was supported by the Ministry of Science and Technology under Grant MOST 103-2221-E-018-017- and MOST 105-2221-E-018-023 .

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, National Changhua University of Education, Changhua, 500, Taiwan

    Ming-Tsung Yeh, Yun-Jhong Hu & Yi-Nung Chung

  2. Changhua Christian Hospital, Changhua, 500, Taiwan

    Chien-Wen Lai

  3. Department of Information and Network Communications, Chienkuo Technology University, Changhua, 500, Taiwan

    Chao-Hsing Hsu

Authors
  1. Ming-Tsung Yeh
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  2. Yun-Jhong Hu
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  3. Chien-Wen Lai
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  4. Chao-Hsing Hsu
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  5. Yi-Nung Chung
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Corresponding author

Correspondence to Chao-Hsing Hsu .

Editor information

Editors and Affiliations

  1. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, Fujian, China

    Jeng-Shyang Pan

  2. School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong, China

    Jerry Chun-Wei Lin

  3. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, Fujian, China

    Chia-Hung Wang

  4. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, Fujian, China

    Xin Hua Jiang

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Yeh, MT., Hu, YJ., Lai, CW., Hsu, CH., Chung, YN. (2017). Apply Image Technology to River Level Estimation. In: Pan, JS., Lin, JW., Wang, CH., Jiang, X. (eds) Genetic and Evolutionary Computing. ICGEC 2016. Advances in Intelligent Systems and Computing, vol 536. Springer, Cham. https://doi.org/10.1007/978-3-319-48490-7_11

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  • DOI: https://doi.org/10.1007/978-3-319-48490-7_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48489-1

  • Online ISBN: 978-3-319-48490-7

  • eBook Packages: EngineeringEngineering (R0)

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