WEKO3
アイテム
Cycle-Generative Adversarial Network for Generating a Pseudo Realistic Food Dataset Using RGB and Depth Images
http://hdl.handle.net/10228/0002001340
http://hdl.handle.net/10228/0002001340b35139f5-be15-4c69-b770-c690b044d013
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
10444483.pdf (811 KB)
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| Item type | 共通アイテムタイプ(1) | |||||||||||||
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| 公開日 | 2025-02-18 | |||||||||||||
| タイトル | ||||||||||||||
| タイトル | Cycle-Generative Adversarial Network for Generating a Pseudo Realistic Food Dataset Using RGB and Depth Images | |||||||||||||
| 言語 | en | |||||||||||||
| 著者 |
Al aama, Obada
× Al aama, Obada
× Yoshimoto, Yuma
× 田向, 権
WEKO
6059
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| 著作権関連情報 | ||||||||||||||
| 権利情報 | Copyright (c) 2022 The Author. Published by Sugisaka Masanori at ALife Robotics Corporation Ltd. | |||||||||||||
| 著作権関連情報 | ||||||||||||||
| 権利情報Resource | http://creativecommons.org/licenses/by-nc/4.0/ | |||||||||||||
| 権利情報 | This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/). | |||||||||||||
| 抄録 | ||||||||||||||
| 内容記述タイプ | Abstract | |||||||||||||
| 内容記述 | Constructing a food dataset is time and effort consuming due to the requirement for covering the feature variations of food samples. Additionally, a large dataset is needed for training neural networks. Generative adversarial networks (GANs) are a recently developed technique to learn deep representations without extensively annotated training data. They can be used in several applications, including generating food datasets. This paper advocates the use of Cycle-GAN to generate a large pseudo-realistic food dataset based on a large number of simulated images and a small number of real images in comparison to traditional techniques. A single depth camera in three different angles and a turntable are arranged to capture real RGB-D images of food samples. 3D modeling software is used to generate simulated images using the same configuration of captured real images. Results showed that Cycle-GAN realistic style transfer on simulated food objects is achievable, and that it can be an efficient tool to minimize real image capturing efforts. | |||||||||||||
| 言語 | en | |||||||||||||
| 書誌情報 |
en : Journal of Advances in Artificial Life Robotics 巻 2, 号 3, p. 128-133, 発行日 2021-12 |
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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_6501 | |||||||||||||
| 資源タイプ | journal article | |||||||||||||
| 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/10.57417/jaalr.2.3_128 | |||||||||||||
| CRID | ||||||||||||||
| 識別子タイプ | CRID | |||||||||||||
| 関連識別子 | https://cir.nii.ac.jp/crid/1390856660868705920 | |||||||||||||
| ISSN | ||||||||||||||
| 収録物識別子タイプ | EISSN | |||||||||||||
| 収録物識別子 | 2435-8061 | |||||||||||||
| 査読の有無 | ||||||||||||||
| 値 | yes | |||||||||||||
| 研究者情報 | ||||||||||||||
| URL | https://hyokadb02.jimu.kyutech.ac.jp/html/100000641_ja.html | |||||||||||||
| 論文ID（連携） | ||||||||||||||
| 値 | 10444483 | |||||||||||||
| 連携ID | ||||||||||||||
| 値 | 12780 | |||||||||||||
