The method of rapid measurement of marine zooplankton based on template matching and invariant moment

XU Shuai, YANG Jun-yi, ZHENG Min-hui, XIE Shang-wei

Journal of Marine Sciences ›› 2017, Vol. 35 ›› Issue (4) : 69-75.

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Journal of Marine Sciences ›› 2017, Vol. 35 ›› Issue (4) : 69-75. DOI: 10.3969/j.issn.1001-909X.2017.04.007

The method of rapid measurement of marine zooplankton based on template matching and invariant moment

  • XU Shuai1,2, YANG Jun-yi1,2, ZHENG Min-hui1,2, XIE Shang-wei1,2
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Abstract

Zooplankton is widely distributed in the marine ecosystem, which plays an important role in global climate change. Zooplankton analysis based on image processing needs rapid development. According to the shape of marine zooplankton microscopic image characteristics, a hybrid method based on template matching and invariant central moment description method was proposed in this study. Template matching was used to define the target initial range, and the final target organisms were determined by comparing the moment invariants. The amount and size of the target were calculated. This method was not affected by the rotation of biological azimuth and the scaling of size. The verified experiments show that the proposed method is fast and efficient, which is suitable for situ zooplankton monitoring in real time.

Key words

zooplankton / template matching / moment invariants

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XU Shuai, YANG Jun-yi, ZHENG Min-hui, XIE Shang-wei. The method of rapid measurement of marine zooplankton based on template matching and invariant moment[J]. Journal of Marine Sciences. 2017, 35(4): 69-75 https://doi.org/10.3969/j.issn.1001-909X.2017.04.007

References

[1] CULVERHOUSE P F, REGUERA B, GONZALEZ G S, et al. Do experts make mistakes[J]. Marine Ecology Progress,2003,247(247):17-25.
[2] LOKE R E, BUF J M H D, BAYER M M, et al. Diatom classification in ecological applications[J]. Pattern Recognition,2004,37(6):1 283-1 285.
[3] HU Q,DAVIS C. Accurate automatic quantification of taxa-specific plankton abundance using dual classification with correction[J]. Marine Ecology Progress,2006,306(8):51-61.
[4] TANG X, STEWART W K, HUANG H, et al. Automatic plankton image recognition[J]. Artificial Intelligence Review,1998,12(1):177-199.
[5] YANG R, ZHANG R, SUN S. Automated classification of zooplankton based on digital image processing[J]. Computer Simulation,2006,23(5):167-170.
[6] RAFAEL C G, RICHARD E W. Digital image processing[M]. Third edition. Beijing: Publishing House of Electronics Industry,2011.
[7] KENNETH R C.Digital image processing[M]. Beijing:Publishing House of Electronics Industry,2002.
[8] OTSU N A. Athreshold selection method from gray-level histograms[J]. IEEE Transactions System Man and Cybemectics,1979,9(1):62-66.
[9] CANNY J. A computational approach to edge detection[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence,1986,8(6):679-98.
[10] CARSTEN S,MARKUS U,CHRISTIAN W. Machine vision algorithms and application[M]. Beijing: Tsinghua University Press,2008.
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