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| Presentation | 2019-01-18 15:55 GANs for Generating Whole Image from One Region of 360-degree Naofumi Akimoto, Masaki Hayashi, Seito Kasai, Yoshimitsu Aoki (Keio Univ.)  PRMU2018-111 MVE2018-53 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In this paper, we present a novel problem setting in which, using one direction of a 360-degree image, a Generative Adversarial Networks (GANs) completes a whole 360-degree image. We also address this problem with a goal which is, distortions of the outlines of roads and buildings that specifically exist in 360-degree images should be generated. Furthermore, for making this problem easy, we present image rearranging which is done using a specific property seen in a 360-degree image. This is that both edge of 360-degree images are originally continuous. And also, we present a combination of dilated convolution layers as effective architectures for generation of a 360-degree image. In our experiments, we show that the series and/or parallel architecture generated better results, in which the white holes seen in baseline results were suppressed and the distortions of the outlines of roads and buildings were generated. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Generative Adversarial Network / deep learning / / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 118, no. 404, PRMU2018-111, pp. 149-153, Jan. 2019. | |
| Paper # | PRMU2018-111 | |
| Date of Issue | 2019-01-10 (PRMU, MVE) | |
| ISSN | Print edition: ISSN 0913-5685 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 | PRMU2018-111 MVE2018-53 |
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| Conference Information | |
| Committee | PRMU MVE IPSJ-CVIM |
| Conference Date | 2019-01-17 - 2019-01-18 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | PRMU |
| Conference Code | 2019-01-PRMU-MVE-CVIM |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | GANs for Generating Whole Image from One Region of 360-degree |
| Sub Title (in English) | |
| Keyword(1) | Generative Adversarial Network |
| Keyword(2) | deep learning |
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| 1st Author's Name | Naofumi Akimoto |
| 1st Author's Affiliation | Keio University (Keio Univ.) |
| 2nd Author's Name | Masaki Hayashi |
| 2nd Author's Affiliation | Keio University (Keio Univ.) |
| 3rd Author's Name | Seito Kasai |
| 3rd Author's Affiliation | Keio University (Keio Univ.) |
| 4th Author's Name | Yoshimitsu Aoki |
| 4th Author's Affiliation | Keio University (Keio Univ.) |
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| Speaker | Author-1 |
| Date Time | 2019-01-18 15:55:00 |
| Presentation Time | 15 minutes |
| Registration for | PRMU |
| Paper # | PRMU2018-111, MVE2018-53 |
| Volume (vol) | vol.118 |
| Number (no) | no.404(PRMU), no.405(MVE) |
| Page | pp.149-153 |
| #Pages | 5 |
| Date of Issue | 2019-01-10 (PRMU, MVE) |
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