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Examination of Phytoplankton Cells by Isolated Cultured Cell Images as Teacher Data
http://hdl.handle.net/10487/00017119
http://hdl.handle.net/10487/0001711988f6fe7a-9995-4385-9c90-efc740354164
名前 / ファイル | ライセンス | アクション |
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03 Examination of Phytoplankton Cells by Isolated Cultured Cell Images as Teacher Data (1.7 MB)
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Item type | 紀要論文 / Departmental Bulletin Paper(1) | |||||
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公開日 | 2021-08-17 | |||||
タイトル | ||||||
言語 | en | |||||
タイトル | Examination of Phytoplankton Cells by Isolated Cultured Cell Images as Teacher Data | |||||
言語 | ||||||
言語 | eng | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | phytoplankton cells | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | segmentation | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | classification | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | mathematical morphology | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | convolutional neural networks | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | deep learning | |||||
キーワード | ||||||
言語 | en | |||||
主題Scheme | Other | |||||
主題 | phytoplankton cells | |||||
キーワード | ||||||
言語 | en | |||||
主題Scheme | Other | |||||
主題 | segmentation | |||||
キーワード | ||||||
言語 | en | |||||
主題Scheme | Other | |||||
主題 | classification | |||||
キーワード | ||||||
言語 | en | |||||
主題Scheme | Other | |||||
主題 | mathematical morphology | |||||
キーワード | ||||||
言語 | en | |||||
主題Scheme | Other | |||||
主題 | convolutional neural networks | |||||
キーワード | ||||||
言語 | en | |||||
主題Scheme | Other | |||||
主題 | deep learning | |||||
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 | departmental bulletin paper | |||||
著者 |
Yu, Yongjie
× Yu, Yongjie× Suzuki, Yoshihiro× Zhang, Shanjun |
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抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | Analysis of the species composition of phytoplankton communities (analy-sis of community structure), which is indispensable for analysis of photosynthesis and CO2 absorption, requires long-term image authentication work by skilled research-ers. If this work becomes possible through image processing with high accuracy and in large quantities, research on photosynthesis and CO2 absorption in aquatic ecosys-tems will markedly advance. In this study, high-quality teacher data are created us-ing isolated cultured cells by biological methods. Using these data, we developed an au-tomatic system to analyze the phytoplankton community structure by deep learning. | |||||
内容記述 | ||||||
内容記述タイプ | Other | |||||
内容記述 | Full-Length Paper | |||||
内容記述 | ||||||
内容記述タイプ | Other | |||||
内容記述 | By a grant from Research Institute for Integrated Science, Kanagawa University | |||||
書誌情報 |
en : Science Journal of Kanagawa University 巻 32, p. 17-20, 発行日 2021-06-30 |
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ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 1880-0483 | |||||
書誌レコードID | ||||||
収録物識別子タイプ | NCID | |||||
収録物識別子 | AA12068302 | |||||
著者版フラグ | ||||||
出版タイプ | VoR | |||||
出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||
出版者 | ||||||
出版者 | 神奈川大学総合理学研究所 | |||||
出版者 | ||||||
出版者 | Research Institute for Integrated Science, Kanagawa University | |||||
資源タイプ | ||||||
内容記述タイプ | Other | |||||
内容記述 | Departmental Bulletin Paper |