Abstract
Automatic pupil detection is a fundamental part of eye-related tasks like eye tracking, gaze estimation and eye movement identification. Especially, in ophthalmology, to provide assistance and fulfil the demand of diagnosis and treatment, an accurate and real-time algorithm is required. In this paper, we propose a fast and robust Multi-Peak Binarization (MPB) based method for pupil detection in ophthalmology scenarios. A novel strategy for region of interest and candidate connected area detection is presented. Constraints for pruning the irregular shapes and accelerating the MPB algorithm are defined. The proposed method is evaluated on an open-dataset and the experimental results demonstrate the high performance of our approach.
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Notes
- 1.
Object 3 and 5, including 6 samples, are dropped out.
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This work is supported by ARC Discovery Project DP200101175 and CSIRO Data61 Grant Australia.
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He, C., Zhou, X., Wang, C. (2020). MPB: Multi-Peak Binarization for Pupil Detection. In: Yang, X., Wang, CD., Islam, M.S., Zhang, Z. (eds) Advanced Data Mining and Applications. ADMA 2020. Lecture Notes in Computer Science(), vol 12447. Springer, Cham. https://doi.org/10.1007/978-3-030-65390-3_22
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