ID | 114106 |
Author |
Ito, Shin-ichi
Tokushima University
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Ito, Momoyo
Tokushima University
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Fukumi, Minoru
Tokushima University
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Keywords | Japanese sign language
gathered image
mean image
convolutional neural network
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Content Type |
Journal Article
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Description | 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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Journal Title |
International Journal of Advances in Intelligent Informatics
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ISSN | 24426571
25483161
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Publisher | Universitas Ahmad Dahlan
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Volume | 5
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Issue | 3
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Start Page | 243
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End Page | 255
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Published Date | 2019-10-29
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Rights | 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 | |
DOI (Published Version) | |
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language |
eng
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TextVersion |
Publisher
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departments |
Science and Technology
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