MetaScleraSeg: an effective meta-learning framework for generalized sclera segmentation-Reference-Cited by-同舟云学术

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MetaScleraSeg: an effective meta-learning framework for generalized sclera segmentation

Published:2023-08-21 Issue:29 Volume:35 Page:21797-21826
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Wang Caiyong^ORCID,Li Haiqing,Ma Wenhui,Zhao Guangzhe,He Zhaofeng

Funder

National Natural Science Foundation of China

Pyramid Talent Training Project of BUCEA

Young Talent Holding Project of Beijing Association For Science and Technology

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

https://link.springer.com/content/pdf/10.1007/s00521-023-08937-8.pdf

Reference84 articles.

1. Liu Y, Shen W, Wu D, Shao J (2022) Irisst-net for iris segmentation and contour parameters extraction. Appl Intell 1–15

2. Rot P, Vitek M, Grm K, Emeršič Ž, Peer P, Štruc V (2020) In: Uhl, A., Busch, C., Marcel, S., Veldhuis, R. (eds.) Deep Sclera Segmentation and Recognition, pp. 395–432. Springer, Cham

3. Das S, Ghosh ID, Chattopadhyay A (2021) An efficient deep sclera recognition framework with novel sclera segmentation, vessel extraction and gaze detection. Signal Process Image Commun 97:116349

4. Zhu D, Li J, Li H, Peng J, Wang X, Zhang X (2021) A less-constrained sclera recognition method based on stem-and-leaf branches network. Pattern Recogn Lett 145:43–49

5. Lee S, Low CY, Kim J, Teoh ABJ (2022) Robust sclera recognition based on a local spherical structure. Expert Syst Appl 189:116081

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