ID | 114106 |
著者 | |
キーワード | Japanese sign language
gathered image
mean image
convolutional neural network
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資料タイプ |
学術雑誌論文
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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.
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掲載誌名 |
International Journal of Advances in Intelligent Informatics
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ISSN | 24426571
25483161
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出版者 | Universitas Ahmad Dahlan
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巻 | 5
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号 | 3
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開始ページ | 243
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終了ページ | 255
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発行日 | 2019-10-29
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権利情報 | This is an open access article under the CC–BY-SA license(https://creativecommons.org/licenses/by-sa/4.0/).
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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
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著者版フラグ |
出版社版
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部局 |
理工学系
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