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ID 114106
著者
キーワード
Japanese sign language
gathered image
mean image
convolutional neural network
資料タイプ
学術雑誌論文
抄録
This paper proposes a method to classify words in Japanese Sign Language (JSL). This approach employs a combined gathered image generation technique and a neural network with convolutional and pooling layers (CNNs). The gathered image generation generates images based on mean images. Herein, the maximum difference value is between blocks of mean and JSL motions images. The gathered images comprise blocks that having the calculated maximum difference value. CNNs extract the features of the gathered images, while a support vector machine for multi-class classification, and a multilayer perceptron are employed to classify 20 JSL words. The experimental results had 94.1% for the mean recognition accuracy of the proposed method. These results suggest that the proposed method can obtain information to classify the sample words.
掲載誌名
International Journal of Advances in Intelligent Informatics
ISSN
24426571
25483161
出版者
Universitas Ahmad Dahlan
5
3
開始ページ
243
終了ページ
255
発行日
2019-10-29
権利情報
This is an open access article under the CC–BY-SA license(https://creativecommons.org/licenses/by-sa/4.0/).
EDB ID
出版社版DOI
出版社版URL
フルテキストファイル
言語
eng
著者版フラグ
出版社版
部局
理工学系