Open Access
MATEC Web Conf.
Volume 232, 2018
2018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)
Article Number 03042
Number of page(s) 6
Section Algorithm Study and Mathematical Application
Published online 19 November 2018
  1. Grobel K, Assan M. Isolated sign language recognition using hidden Markov models[C]. IEEE International Conference on Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation. IEEE, 2002:162-167 vol.1. [Google Scholar]
  2. Reyes M, Dominguez G, Escalera S. Featureweighting in dynamic timewarping for gesture recognition in depth data[C]. IEEE International Conference on Computer Vision Workshops. IEEE, 2011:1182-1188. [Google Scholar]
  3. Simo-Serra E, Ramisa A, Alenyà G, et al. Single image 3D human pose estimation from noisy observations[C] Computer Vision and Pattern Recognition. IEEE, 2012:2673-2680. [Google Scholar]
  4. Sinha A, Choi C, Ramani K. Deephand: Robust hand pose estimation by completing a matrix imputed with deep features[C]. Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 4150-4158. [Google Scholar]
  5. Koren Y. The bellkor solution to the netflix grand prize[J]. Netflix Prize Documentation, 2009. [Google Scholar]
  6. Zhu Yuanxin, Xu Guangyou, Huang Huangyou, etc. Dynamic isolated gesture recognition based on appearance[J]. Journal of Software,2000,11 (1): 54-61. [Google Scholar]
  7. Xiao Ling, Li Renfa, Zeng Fanzi, Qu Weilan. Dynamic gesture recognition method based on learning sparse representation[J]. Journal on Communications, 2013, 34(6): 128-135. [Google Scholar]
  8. Liu Shuping, Liu Yu,Yu Jun, Wang Zengfu. Hierarchical static gesture recognition combining finger detection and HOG feature[J]. Journal of Image and Graphics, 2015, 20(6) : 0781-0788. [Google Scholar]
  9. Wang Kai, Yu Hongyang, Zhang Ping. Real-time gesture recognition based on AdaBoost algorithm and optical flow matching[J]. Microelectronics & Computer. 2012. 5(29):138-141. [Google Scholar]
  10. Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[C]. Proceeding s of the IEEE conference on computer vision and pat tern recognition. 2015: 3431-3440. [Google Scholar]
  11. Badrinarayanan V, Handa A, Cipolla R. Segnet: A deep convolutional encoder-decoder architecture for robust semantic pixel-wise labelling[J]. arXiv preprint arXiv:1505.07293, 2015. [Google Scholar]
  12. Oberweger M, Lepetit V. DeepPrior++: Improving Fast and Accurate 3D Hand Pose Estimation[J]. 2017:585-594. [Google Scholar]
  13. Zimmermann C, Brox T. Learning to Estimate 3D Hand Pose from Single RGB Images[J]. 2017:4913-4921. [Google Scholar]
  14. Wei S E, Ramakrishna V, Kanade T, et al. Convolutional pose machines[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016: 4724-4732. [Google Scholar]
  15. Oberweger M, Wohlhart P, Lepetit V. Hands Deep in Deep Learning for Hand Pose Estimation[J]. Computer Science, 2016. [Google Scholar]
  16. Li Qing, Tang Huan, Chi Jian Nan, Xing Yongyue, Li Huatong. Study on the method of gesture segmentation based on the improved maximum category variance method [J]. Acta Automatica Sinica, 2017, 43(4): 528-537 [Google Scholar]
  17. Mao Yanming, Zhang Liliang. Gesture segmentation and recognition based on Kinect depth information[J]. Journal of System Simulation, 2015, 27(4): 830-835 [Google Scholar]
  18. Zhang Yi, Yao Yuanyuan, Luo Yuan. HMM dynamic gesture recognition algorithm based on B parameter improvement[J]. Journal of Huazhong University of Science and Technology (Natural Science Edition). 2015(10): 416-419. [Google Scholar]
  19. Wang Xiying, Dai Guozhong, Zhang Xiwen, et al. Complex dynamic gesture recognition based on HMM-FNN model[J]. Journal of Software, 2008, 19 (9): 2302-2312. [CrossRef] [Google Scholar]
  20. Zhang Yi, Wu Shihai, Luo Yuan. The method and application of signal track recognition based on HMM[J]. Electro-Optic Technology Application. 2015 (8): 650-656. [Google Scholar]
  21. Xu Jiabin. Research on dynamic gesture recognition based on parallel HMM[D]. Beijing University of Posts and Telecommunications. 2016. [Google Scholar]
  22. Yu Meijuan, Ma Xirong. Improvement of dynamic gesture recognition based on HMM method [J]. Computer Science. 2011(1):251-252. [Google Scholar]
  23. Wang Wanliang, Yang Jingwei and Jiang Yibo. Gesture recognition based on motion sensor[J]. Chinese Journal of Sensors and Actuators, 2011, 24(12): 1723-1727. [Google Scholar]
  24. Mueller F, Bernard F, Sotnychenko O, et al. GANerated Hands for Real-time 3D Hand Tracking from Monocular RGB[J]. 2017. [Google Scholar]
  25. Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio. Generative Adversrial Nets[C].International Conference on Neural Information Processing Systems. 2014,3:2672-2680. [Google Scholar]
  26. Spurr A, Song J, Park S, et al. Cross-modal Deep Variational Hand Pose Estimation[J]. 2018. [Google Scholar]
  27. Kendall A, Grimes M, Cipolla R. Convolutional networks for real-time 6-DOF camera relocalization. CoRR abs/1505.07427 (2015)[J]. 2015. [Google Scholar]
  28. Xue junjie and Chen jianqiang. Research and implementation of weighted DTW gesture recognition method[J]. Information Technology. 2015(11):125-129. [Google Scholar]
  29. Yu Chao, Guan Shengxiao. Dynamic gesture tracking recognition based on TLD and DTW[J]. Computer Systems & Applications. 2015. 10(24):148-154. [Google Scholar]
  30. Li Kai, Wang Yongxiong, Sun Yipin. An improved DTW dynamic gesture recognition method[J]. Journal of Chinese Computer Systems. 2016. 7(7):1600-1603. [Google Scholar]
  31. He Chao, Hu Zhangfang and Wang Yan. A dynamic gesture recognition method based on improved DTW algorithm[J]. Digital Communication. 2013. 6(40):21-25. [Google Scholar]
  32. Zhou Zhiping and Miao Minmin. Combined DTW and improved STLCS dynamic gesture authentication[J]. Journal of Electronic Measurement and Instrumentation. 2015. 7(29):1064-1073. [Google Scholar]

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