Laser & Optoelectronics Progress, Volume. 55, Issue 11, 111010(2018)

Sign Language Semantic Recognition Based on Optimized Fully Convolutional Neural Network

Min Wang, Jing Hao*, Chenhong Yao, and Qiqi Shi
Author Affiliations
  • School of Information and Control Engineering, Xi'an University of Architecture and Technology, Xi'an, Shaanxi 710055, China
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    References(23)

    [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).

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    [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).

    [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).

    [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).

    [22] Zeng W J, Qin F, Si Y[J]. Sign language recognition system based on artificial neural network China Computer & Communication, 2017, 143-144.

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    Min Wang, Jing Hao, Chenhong Yao, Qiqi Shi. Sign Language Semantic Recognition Based on Optimized Fully Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2018, 55(11): 111010

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    Paper Information

    Category: Image Processing

    Received: May. 3, 2018

    Accepted: Jun. 8, 2018

    Published Online: Aug. 14, 2019

    The Author Email: Jing Hao (haoanjing12@163.com)

    DOI:10.3788/LOP55.111010

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