Journal of the Institute of Convergence Signal Processing (융합신호처리학회논문지)

The Korea Institute of Convergence Signal Processing (한국융합신호처리학회)
권호 표지

Multiple Plankton Detection and Recognition in Microscopic Images with Homogeneous Clumping and Heterogeneous Interspersion

  • Soh, Youngsung (Department of Information and Communication Eng., Myongji University) ;
  • Song, Jaehyun (Artificial Intelligence Lab in DIPS Co.) ;
  • Hae, Yongsuk (Artificial Intelligence Lab in DIPS Co.)
  • Received : 2018.05.18
  • Accepted : 2018.06.24
  • Published : 2018.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

The analysis of plankton species distribution in sea or fresh water is very important in preserving marine ecosystem health. Since manual analysis is infeasible, many automatic approaches were proposed. They usually use images from in situ towed underwater imaging sensor or specially designed, lab mounted microscopic imaging system. Normally they assume that only single plankton is present in an image so that, if there is a clumping among multiple plankton of same species (homogeneous clumping) or if there are multiple plankton of different species scattered in an image (heterogeneous interspersion), they have a difficulty in recognition. In this work, we propose a deep learning based method that can detect and recognize individual plankton in images with homogeneous clumping, heterogeneous interspersion, or combination of both.

Keywords

References

  1. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You only look once: Unified, real-time object detection," arXiv preprint arXiv:1506.02640, 2015.
  2. R. Girshick, "Fast R-CNN," Proc. of IEEE International Conference on Computer Vision (ICCV), 2015.
  3. S. Ren, K. He, R. Girshick, and J. Sun,"Faster r-cnn: Towards real-time object detection with region proposal networks," arXiv preprint arXiv:1506.01497, 2015.
  4. http://www.fluidimaging.com/
  5. T. Luo, K. Kramer, D. B. Goldgof, L. O. Hall, S. Samson, A. Remsen, and T. Hopkins, "Active Learning to Recognize Multiple Types of Plankton," Journal of Machine Learning Research, pp.589-613, vol. 6, 2005.
  6. Q. Hu1 and C. Davis, "Automatic plankton image recognition with co-occurrence matrices and support vector machine," Marine Ecology Progress Series, Mar, pp.21-31, vol. 295, 2005. https://doi.org/10.3354/meps295021
  7. M. B. Blaschko, G. Holness, M. A. Mattar, D. Lisin, P. E. Utgoff, A. R. Hanson, H. Schultz, and E. M. Riseman,"Automatic In Situ Identification of Plankton," Proc. of Seventh IEEE Workshops on Application of Computer Vision, pp.79-86, vol. 1, 2005.
  8. R. K. Cowen* and C. M. Guigand," In situ ichthyoplankton imaging system (ISIIS): system design and preliminary results," Limnology and Oceanography: Methods, pp.126-132, vol. 6, Issue, 2 2008. https://doi.org/10.4319/lom.2008.6.126
  9. K. Schulze, U. M Tillich, T. Dandekar, and M. Frohme,"PlanktoVision - an automated analysis system for the identification of phytoplankton," BMC Bioinformatics, 201314:115, 2013. https://doi.org/10.1186/1471-2105-14-115
  10. https://www.kaggle.com/c/datasciencebowl
  11. http://image-net.org/
  12. http://mscoco.org/
  13. http://cs231n.stanford.edu/reports2016/263_Report.pdf