Open Access
Issue |
E3S Web Conf.
Volume 491, 2024
International Conference on Environmental Development Using Computer Science (ICECS’24)
|
|
---|---|---|
Article Number | 04022 | |
Number of page(s) | 12 | |
Section | Engineering for Environment Development Applications | |
DOI | https://doi.org/10.1051/e3sconf/202449104022 | |
Published online | 21 February 2024 |
- Fronteddu, Graziano, Simone Porcu, Alessandro Floris, and Luigi Atzori. (2022). “A Dynamic Hand Gesture Recognition Dataset for Human-Computer Interfaces.” Computer Networks 205 (March): 108781. [CrossRef] [Google Scholar]
- Ghotkar, Archana S., Rucha Khatal, Sanjana Khupase, Surbhi Asati, and Mithila Hadap. (2012). “Hand Gesture Recognition for Indian Sign Language.” In 2012 International Conference on Computer Communication and Informatics, 1–4. [Google Scholar]
- Hussain, Soeb, Rupal Saxena, Xie Han, Jameel Ahmed Khan, and Hyunchul Shin. 2017. “Hand Gesture Recognition Using Deep Learning.” In 2017 International SoC Design Conference (ISOCC), 48–49. [Google Scholar]
- Ikram, Aamrah, and Yue Liu. (2021). “Real Time Hand Gesture Recognition Using Leap Motion Controller Based on CNN-SVM Architechture.” In 2021 IEEE 7th International Conference on Virtual Reality (ICVR), 5–9. [Google Scholar]
- Krishnamoorthi, M., S. Gowtham, K. Sanjeevi, and R. Revanth Vishnu. (2022). “Virtual Mouse Using YOLO.” In 2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), 1–7. [Google Scholar]
- Leelakittisin, Benjakarn, Theerawit Wilaiprasitporn, and Thapanun Sudhawiyangkul. (2021). “Compact CNN for Rapid Inter-Day Hand Gesture Recognition and Person Identification from sEMG.” In 2021 IEEE Sensors, 1–4. [Google Scholar]
- Leon, David Gonzalez, Jade Groli, Sreenivasa Reddy Yeduri, Daniel Rossier, Romuald Mosqueron, Om Jee Pandey, and Linga Reddy Cenkeramaddi. 2022. “Video Hand Gestures Recognition Using Depth Camera and Lightweight CNN.” IEEE Sensors Journal 22 (14): 14610–19. [CrossRef] [Google Scholar]
- Malgireddy, Manavender R., Jason J. Corso, Srirangaraj Setlur, Venu Govindaraju, and Dinesh Mandalapu. (2010). “A Framework for Hand Gesture Recognition and Spotting Using Sub-Gesture Modeling.” In 2010 20th International Conference on Pattern Recognition, 3780–83. [Google Scholar]
- Nadjib, Boucetta Lakhdar, Chemloul Bilal, and Rebai Karima. (2021). “EMGBased Hand Gesture Recognition for Myoelectric Prosthetic Hand Control.” In 2021 International Conference on Artificial Intelligence for Cyber Security Systems and Privacy (AI-CSP), 1–6. [Google Scholar]
- Naguri, Chinmaya R., and Razvan C. Bunescu. (2017). “Recognition of Dynamic Hand Gestures from 3D Motion Data Using LSTM and CNN Architectures.” In 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), 1130–33. [Google Scholar]
- Nayan, Navneet, Debashis Ghosh, and Pyari Mohan Pradhan.(2022). “A CNN Bi-LSTM Based Multimodal Continuous Hand Gesture Recognition.” In 2022 IEEE 12 of 17 India Council International Subsections Conference (INDISCON), 1–4. [Google Scholar]
- Quan, Chunying, and Jianning Liang. (2016). “A Simple and Effective Method for Hand Gesture Recognition.” In 2016 International Conference on Network and Information Systems for Computers (ICNISC), 302–5. [Google Scholar]
- Sai Nikhil, Chinnam Datta, Chukka Uma Someswara Rao, E. Brumancia, K. Indira, T. Anandhi, and P. Ajitha. (2020). “Finger Recognition and Gesture Based Virtual Keyboard.” In 2020 5th International Conference on Communication and Electronics Systems (ICCES), 1321–24. [Google Scholar]
- Shrivastava, Rajat. (2013). “A Hidden Markov Model Based Dynamic Hand Gesture Recognition System Using OpenCV.” In 2013 3rd IEEE International Advance Computing Conference (IACC), 947–50. [Google Scholar]
- Wong, W. K., Filbert H. Juwono, and Brendan Teng Thiam Khoo. (2021). “Multi-Features Capacitive Hand Gesture Recognition Sensor: A Machine Learning Approach.” IEEE Sensors Journal 21 (6): 8441–50. [CrossRef] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.