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作者:

Yang, Su (Yang, Su.) | Zhu, Qing (Zhu, Qing.)

收录:

EI Scopus

摘要:

Convolutional neural network (CNN) has been widely used in computer vision tasks recently and achieved remarkable success. This paper presents a novel video-based recognition approach using CNN for Chinese Sign Language (CSL). The proposed method extracts upper body images directly from videos, and employs a pre-Training convolutional network model to recognize the gesture in the image. The new method simplifies the hand-shape segmentation, and avoid information loss in feature extraction. We evaluate the method on our self-built dataset includes 40 daily vocabularies, and show that the proposed approach has good performance on sign language recognition task, with accuracy reaching to 99%. © 2017 IEEE.

关键词:

Convolution Convolutional neural networks Deep learning Deep neural networks

作者机构:

  • [ 1 ] [Yang, Su]Faculty of Information Technology, Beijing University of Technology, Beijing, China
  • [ 2 ] [Zhu, Qing]Faculty of Information Technology, Beijing University of Technology, Beijing, China

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来源 :

年份: 2017

卷: 2017-January

页码: 929-934

语种: 英文

被引次数:

WoS核心集被引频次: 0

SCOPUS被引频次: 28

ESI高被引论文在榜: 0 展开所有

万方被引频次:

中文被引频次:

近30日浏览量: 2

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