Open Access
| Issue |
MATEC Web Conf.
Volume 218, 2018
The 1st International Conference on Industrial, Electrical and Electronics (ICIEE 2018)
|
|
|---|---|---|
| Article Number | 02014 | |
| Number of page(s) | 7 | |
| Section | Control Electronics, Circuits, and Systems | |
| DOI | https://doi.org/10.1051/matecconf/201821802014 | |
| Published online | 26 October 2018 | |
- R. Passarella and M. Fadli, “Hand Gesture Recognition as Password to Open The Door With Camera and Convexity Defect Method,” Proceeding of The 1st International Conference on Computer Science and Engineering, pp. 69–73 (2014). [Google Scholar]
- H. J. Ahn, J. S. Kim, J.-Y. Shim, and J. S. Kim, “Hand Gesture Recognition for Doors with Neural Network,” Proc. Int. Conf. Res. Adapt. Converg. Syst., pp. 15–18 (2017). [Google Scholar]
- A. Chaudhary, K. Vatwani, T. Agrawal, and J. L. Raheja, “A vision-based method to find fingertips in a closed hand,” J. Inf. Process. Syst., vol. 8, no. 3, pp. 399–408 (2012). [CrossRef] [Google Scholar]
- T. Q. Vinh and N. T. Tri, “Hand gesture recognition based on depth image using kinect sensor,” in 2015 2nd National Foundation for Science and Technology Development Conference on Information and Computer Science (NICS), pp. 34–39 (2015). [CrossRef] [Google Scholar]
- Yi Li, “Hand gesture recognition using Kinect,” 2012 IEEE Int. Conf. Comput. Sci. Autom. Eng., pp. 196–199 (2012). [Google Scholar]
- N. B. Bhoyar and P. M. Bartere, “Neural Network based Static Hand Gesture Recognition,” Int. J. Innov. Res. Comput. Commun. Eng., vol. 3, no. 2, pp. 112–116 (2014). [Google Scholar]
- N. Febriana, A. Rizal, E. Susanto, “Sleep Monitoring System Based on Body Posture Movement using Microsoft Kinect Sensor,” Int. Symp. Biomed. Eng, vol. 3 (2018). [Google Scholar]
- J. Chen, B. Guan, H. Wang, X. Zhang, Y. Tang, and W. Hu, “Image Thresholding Segmentation Based on Two Dimensional Histogram Using Gray Level and Local Entropy Information,” IEEE Access, vol. 6, no. c, pp. 5269–5275 (2017). [CrossRef] [Google Scholar]
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