Author:
Li Jun,Wei Lixin,Wen Yintang,Liu Xiaoguang,Wang Hongrui
Funder
the National Key Research and Development Program of China
the Key Project of Hebei Province Department of Education
Jilin Scientific and Technological Development Program
Key Laboratory of Microbial Resources and Drug Development in Guizhou Province
Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition,Software
Reference45 articles.
1. Park, H., An, B., Baek, J., Lee, D., Seo, J.: Selection of grasping target and control system of robotic prosthetic hand using images and deep learning. J. Inst. Control 26(5), 312–317 (2020)
2. Rheem, H., Becker, D.V., Craig, S.D.: Assessing learning effort with hand motion tracking methods. Appl. Cogn. Psychol. 35, 606–620 (2021). https://doi.org/10.1002/acp.3784
3. Vishwakarma, D.K., and Grover, V.: Hand gesture recognition in low-intensity environment using depth images, in Editor (Ed.)^(Eds.): Book Hand gesture recognition in low-intensity environment using depth images, 429–433(2017, edn.)
4. Jain, R., Karsh, R.K., Barbhuiya, A.A.: Encoded motion image-based dynamic hand gesture recognition. Vis. Comput. (2021). https://doi.org/10.1007/s00371-021-02259-3
5. Zhang W, Wang J, Lan F (2021) Dynamic hand gesture recognition based on short-term sampling neural networks, IEEE/CAA J. Autom. Sin., 8, (01), 114-124. DOI: https://doi.org/10.1109/JAS.2020.1003465
Cited by
14 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献