WEKO3
アイテム
Synthesis of realistic food dataset using generative adversarial network based on RGB and depth images
http://hdl.handle.net/10228/0002001361
http://hdl.handle.net/10228/000200136172b8e8f7-e3ee-4942-b79e-36e4a910a1d0
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
10444508.pdf (669 KB)
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| アイテムタイプ | 共通アイテムタイプ(1) | |||||||||||
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| 公開日 | 2025-02-19 | |||||||||||
| タイトル | ||||||||||||
| タイトル | Synthesis of realistic food dataset using generative adversarial network based on RGB and depth images | |||||||||||
| 言語 | en | |||||||||||
| 著者 |
Al aama, Obada
× Al aama, Obada
× 田向, 権
WEKO
6059
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| 著作権関連情報 | ||||||||||||
| 権利情報 | Copyright (c) The authors. | |||||||||||
| 著作権関連情報 | ||||||||||||
| 権利情報Resource | https://creativecommons.org/licenses/by-nc/4.0/ | |||||||||||
| 権利情報 | This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/ | |||||||||||
| 抄録 | ||||||||||||
| 内容記述タイプ | Abstract | |||||||||||
| 内容記述 | Constructing a large food dataset is time and effort consuming due to the need to cover the feature variations of food items. Hence, a huge data is needed for training neural networks. This paper aims to advocate the Cycle-GAN to build up large food dataset based on large number of simulated images and relatively few real captured images thus obtaining more realistic images effortlessly compared with traditional capturing. Real RGB and depth images of variant food samples allocated over turntable were captured in three different angles using real-sense depth camera with different backgrounds. Furthermore, for simulated images, the Autodesk 3D_Maya software was employed using the same parameters of captured real images. Results showed that generally, realistic style transfer on simulated food objects was obtained as a result of employing Cycle-GAN. GAN proved to be an efficient tool that could minimize imaging efforts resulting in realistic images. | |||||||||||
| 言語 | en | |||||||||||
| 備考 | ||||||||||||
| 内容記述タイプ | Other | |||||||||||
| 内容記述 | The 2021 International Conference on Artificial Life and Robotics (ICAROB 2021), January 21-24, 2021, Higashi-Hiroshima (オンライン開催に変更) | |||||||||||
| 言語 | en | |||||||||||
| 書誌情報 |
en : Proceedings of International Conference on Artificial Life & Robotics (ICAROB2021) 巻 26, p. 16-19, 発行日 2021-01-21 |
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| 出版社 | ||||||||||||
| 出版者 | ALife Robotics | |||||||||||
| 言語 | en | |||||||||||
| キーワード | ||||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | Cycle-GAN | |||||||||||
| キーワード | ||||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | Food dataset | |||||||||||
| キーワード | ||||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | RGB-D images | |||||||||||
| 言語 | ||||||||||||
| 言語 | eng | |||||||||||
| 資源タイプ | ||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_5794 | |||||||||||
| 資源タイプ | conference paper | |||||||||||
| 出版タイプ | ||||||||||||
| 出版タイプ | VoR | |||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||
| DOI | ||||||||||||
| 識別子タイプ | DOI | |||||||||||
| 関連識別子 | https://doi.org/10.5954/icarob.2021.os19-4 | |||||||||||
| ISBN | ||||||||||||
| 識別子タイプ | ISBN | |||||||||||
| 関連識別子 | 978-4-9908350-6-4 | |||||||||||
| ISSN | ||||||||||||
| 収録物識別子タイプ | EISSN | |||||||||||
| 収録物識別子 | 2435-9157 | |||||||||||
| 会議記述 | ||||||||||||
| 会議名 | The 2021 International Conference on Artificial Life and Robotics (ICAROB 2021) | |||||||||||
| 言語 | en | |||||||||||
| 開始年 | 2021 | |||||||||||
| 開始月 | 01 | |||||||||||
| 開始日 | 21 | |||||||||||
| 終了年 | 2021 | |||||||||||
| 終了月 | 01 | |||||||||||
| 終了日 | 24 | |||||||||||
| 研究者情報 | ||||||||||||
| URL | https://hyokadb02.jimu.kyutech.ac.jp/html/100000641_ja.html | |||||||||||
| 論文ID（連携） | ||||||||||||
| 値 | 10444508 | |||||||||||
| 連携ID | ||||||||||||
| 値 | 12804 | |||||||||||
