Journal of the Korean Society for Industrial and Applied Mathematics

  • 제25권4호
  • /
  • Pages.327-336
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  • 2021
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  • 1226-9433(pISSN)
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  • 1229-0645(eISSN)
한국산업응용수학회 (Korean Society for Industrial and Applied Mathematics)
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A STUDY ON PUPIL DETECTION AND TRACKING METHODS BASED ON IMAGE DATA ANALYSIS

  • CHOI, HANA (DEPARTMENT OF INNOVATION CENTER FOR INDUSTRIAL MATHEMATICS, NATIONAL INSTITUTE FOR MATHEMATICAL SCIENCES) ;
  • GIM, MINJUNG (DEPARTMENT OF INNOVATION CENTER FOR INDUSTRIAL MATHEMATICS, NATIONAL INSTITUTE FOR MATHEMATICAL SCIENCES) ;
  • YOON, SANGWON (DEPARTMENT OF RESEARCH UNIT, DN CORPORATION)
  • 투고 : 2021.11.22
  • 심사 : 2021.12.15
  • 발행 : 2021.12.25
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초록

In this paper, we will introduce the image processing methods for the remote pupillary light reflex measurement using the video taken by a general smartphone camera without a special device such as an infrared camera. We propose an algorithm for estimate the size of the pupil that changes with light using image data analysis without a learning process. In addition, we will introduce the results of visualizing the change in the pupil size by removing noise from the recorded data of the pupil size measured for each frame of the video. We expect that this study will contribute to the construction of an objective indicator for remote pupillary light reflex measurement in the situation where non-face-to-face communication has become common due to COVID-19 and the demand for remote diagnosis is increasing.

키워드

과제정보

The work of H. Choi and M. Gim was supported by National Institute for Mathematical Sciences(NIMS) grant funded by the Korea government( MSIT ) No.B21810000. The work of S. Yoon was supported by the technology transfer and commercialization Program through INNOPOLIS Foundation funded by the Ministry of Science and ICTm 2020-JB-RD-0199.

참고문헌

  1. D. Li, D. Winfield and D. J. Parkhurst, Starburst: A hybrid algorithm for video-based eye tracking combining feature-based and model-based approaches, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, (2005), 79-79.
  2. C. Harris and M. Stephens, A combined corner and edge detector, Alvey Vision Conference, (1988).
  3. Lowe, D.G., Distinctive image features from scale-invariant keypoints, International Journal of Computer Vision, (2004).
  4. D. Temel, M. J. Mathew, G. AlRegib and Y. Khalifa, Automated Pupillary Light Reflex Test on a Portable Platform, International Symposium on Medical Robotics, (2019), 1-7.
  5. N. Kim, H. Lee, S. Im, C. Moon and Y. Nam, A Preliminary Study on Pupillary Light Reflex Measurement using a Smartphone Camera, Proceedings of the Korea Information Processing Society Conference (2015), 534-537.
  6. P. Viola and M. Jones, Rapid Object Detection using a Boosted Cascade of Simple Features, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1 (2001).
  7. N. M. Ali, N. K.M.Rashid and Y. M. Mustafah, Performance Comparison between RGB and HSV Color Segmentations for Road Signs Detection, Applied Mechanics and Materials, 393 (2013), 550-555. https://doi.org/10.4028/www.scientific.net/AMM.393.550
  8. P. J. Rousseeuw, Least median of squares regression, Journal of the American Statistical Association, 79 (1984), 871-880. https://doi.org/10.1080/01621459.1984.10477105
  9. R. O. Duda and P E. Hart, Use of the Hough Transformation to Detect Lines and Curves in Pictures, Communications of the ACM, 15 (1972), 11-15. http ://docs.opencv.org/4.3.0/db/d28/tutorial_cascade_classifier.html https://doi.org/10.1145/361237.361242