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| Presentation | 2019-09-06 14:20 A Machine Learning-based Character Identification System for the Visually Impaired to Enjoy Broadcast Animations Yu Yoshino, Kazuki Nakada, Makoto Kobayashi, Iwao Sekita, Hisayuki Tatsumi (NTUT)  ET2019-31 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | This study aims to assist visually impaired individuals by focusing on the following problems that arise at the time of viewing animation videos and images. (1) difficulty of understanding behaviors and situations, (2) difficulty of discriminating animation characters, and (3) confusion caused by animation characters with similarities. To identify the target animation character on the above problems, we are going to make a support equipment for character identification using machine learning. The machine learning framework has been constructed so that students as users can achieve machine learning with desired animation datasets themselves. In our framework, we combined the area detection using a cascade classifier and the face discrimination using deep learning to balance of the easiness of learning and the robustness of discrimination accuracy. For efficient learning from desired small dataset of animation characters, we applied the transfer learning to the extended convolutional neural network (CNN) model pre-trained with ImageNet, and we confirmed that the bottleneck features of the learned CNN model are effective for identifying animation characters. Moreover, we implemented the customized CNN model trained by a student himself on a hardware accelerator and verified the real time operation in practical environments. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Visual impairment assistance / Animation character / Automatic identification / Deep Neural Network / Transfer learning / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 119, no. 200, ET2019-31, pp. 35-40, Sept. 2019. | |
| Paper # | ET2019-31 | |
| Date of Issue | 2019-08-30 (ET) | |
| ISSN | Online edition: ISSN 2432-6380 | |
| Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| Download PDF | ET2019-31 |
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| Conference Information | |
| Committee | ET |
| Conference Date | 2019-09-06 - 2019-09-06 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Tsukuba University of Technology |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Special Needs Education and Welfare Support, etc. |
| Paper Information | |
| Registration To | ET |
| Conference Code | 2019-09-ET |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A Machine Learning-based Character Identification System for the Visually Impaired to Enjoy Broadcast Animations |
| Sub Title (in English) | |
| Keyword(1) | Visual impairment assistance |
| Keyword(2) | Animation character |
| Keyword(3) | Automatic identification |
| Keyword(4) | Deep Neural Network |
| Keyword(5) | Transfer learning |
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| 1st Author's Name | Yu Yoshino |
| 1st Author's Affiliation | Tsukuba University of Technology (NTUT) |
| 2nd Author's Name | Kazuki Nakada |
| 2nd Author's Affiliation | Tsukuba University of Technology (NTUT) |
| 3rd Author's Name | Makoto Kobayashi |
| 3rd Author's Affiliation | Tsukuba University of Technology (NTUT) |
| 4th Author's Name | Iwao Sekita |
| 4th Author's Affiliation | Tsukuba University of Technology (NTUT) |
| 5th Author's Name | Hisayuki Tatsumi |
| 5th Author's Affiliation | Tsukuba University of Technology (NTUT) |
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| Speaker | Author-5 |
| Date Time | 2019-09-06 14:20:00 |
| Presentation Time | 25 minutes |
| Registration for | ET |
| Paper # | ET2019-31 |
| Volume (vol) | vol.119 |
| Number (no) | no.200 |
| Page | pp.35-40 |
| #Pages | 6 |
| Date of Issue | 2019-08-30 (ET) |
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