[1] Jiang Y J. Research on isolated sign language recognition system based on Kinect[D]. Hefei: University of Science and Technology of China, 1-8(2015).
[2] Zhang S J[M]. Unmarked gesture recognition based on vision, 1-25(2016).
[3] Sun C, Zhang T, Bao B K et al. Latent support vector machine for sign language recognition with Kinect. [C]∥IEEE International Conference on Image Processing, 4190-4194(2014).
[4] Li M. Algorithm of gesture recognition based on image analysis[D]. Beijing: North China University of Technology, 15-28(2015).
[5] Huong T N T, Huu T V, Xuan T L et al. . Static hand gesture recognition for vietnamese sign language (VSL) using principle components analysis. [C]∥International Conference on Communications, Management and Telecommunications, 138-141(2016).
[6] Liu X J, Zhang Y. Gesture recognition based on multi-feature and SVM classification[J]. Computer Engineering and Design, 38, 953-958(2017).
[7] Bao Z Q, Lü C G. Real-time gesture recognition based on Kinect[J]. Laser & Optoelectronics Progress, 55, 031008(2018).
[8] Wang L, Liu H, Wang B et al. Gesture recognition method combining skin color models and convolution neural network[J]. Computer Engineering and Applications, 53, 209-214(2017).
[9] Sang N, Ni Z H. Gesture recognition based on R-FCN in complex scenes[J]. Journal of Huazhong University of Science and Technology(Nature Science Edition), 45, 54-58(2017).
[10] Xiong Z Y, Zhang G F, Wang J Q. Multi-scale feature extract for image sematic segmentation[J]. Journal of South-Central University for Nationalities(Natural Science Edition), 36, 118-124(2017).
[11] Jiao L C, Zhao J, Yang S Y et al[M]. Deep learning, optimization and identification, 77-80(2017).
[12] Maggiori E, Tarabalka Y, Charpiat G et al. Fully convolutional neural networks for remote sensing image classification. [C]∥Geoscience and Remote Sensing Symposium, 5071-5074(2016).
[13] Fang X, Wang G H, Yang H C et al. High resolution remote sensing image classification combining with mean-shift segmentation and fully convolution neural network[J]. Laser & Optoelectronics Progress, 55, 022802(2018).
[14] Milletari F, Navab N, Ahmadi S A. V-Net: fully convolutional neural networks for volumetric medical image segmentation. [C]∥International Conference on 3D Vision, 565-571(2016).
[15] Cao X W, Bo H. Study on gesture recognition based on CNN[J]. Microcomputer & its Applications, 35, 55-57(2016).
[16] Kang K, Wang X. Fully Convolutional Neural Networks for Crowd Segmentation[J]. Computer Science, 49, 25-30(2014). http://cn.arxiv.org/abs/1411.4464
[17] Li S M, Lei G Q, Fan R. Depth map super-resolution reconstruction based on convolutional neural networks[J]. Acta Optica Sinica, 37, 1210002(2017).
[18] Yi M, Sui L C. Aerial image semantic classification method based on improved full convolution neural network[J]. Computer Engineering, 43, 216-221(2017).
[19] Luo B, Gao W, Tang J et al. Learning corner regression-based fully convolutional neural network for license plate localization in complex scene[J]. Journal of Data Acquisition & Processing, 31, 65-72(2016).
[20] Pan C, Wang X J. Summary of algorithms for detecting man-made information in natural background[J]. Optical Technique, 34, 881-885(2008).
[21] Ding H, Zhang X F. Connected handwritten and printed text discrimination in uneven lighted images[J]. Computer Engineering and Design, 33, 4634-4638(2012).
[22] Zeng W J, Qin F, Si Y[J]. Sign language recognition system based on artificial neural network China Computer & Communication, 2017, 143-144.
[23] Xin P, Xu Y L, Tang H et al. Fast airplane detection based on multi-layer feature fusion of fully convolutional networks[J]. Acta Optica Sinica, 38, 031500(2018).