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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 吳泓熹 | zh_TW |
dc.contributor.advisor | Steven Wu | en |
dc.contributor.author | 張廷融 | zh_TW |
dc.contributor.author | Ting-Jung Chang | en |
dc.date.accessioned | 2023-08-15T17:13:31Z | - |
dc.date.available | 2023-11-09 | - |
dc.date.copyright | 2023-08-15 | - |
dc.date.issued | 2023 | - |
dc.date.submitted | 2023-08-07 | - |
dc.identifier.citation | Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021). Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. Journal of big Data, 8, 1-74.
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Zhan, Y., & Zhang, G. (2019). An improved OTSU algorithm using histogram accumulation moment for ore segmentation. Symmetry, 11(3), 431. Zhu, L., & Yang, Y. (2020). Actbert: Learning global-local video-text representations. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 8746-8755). | - |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88651 | - |
dc.description.abstract | 影像分析是一種強大的方法能有效解決多種難題,能有效應用於農業領域。植物影像分析在農業中扮演著重要的角色,適用於植物研究以提高作物產量的研究效率和品質。本研究旨在探索基於植物特徵的演算法能夠快速進行非破壞性取樣 (non-destructive sampling) 進行外表型性狀分析。Otsu為單一通道的影像分割 (image segmentation) 方法,G-Otsu是一種多層次模型,結合了Otsu與多張影像之間的訊息,適合用於整個資料集分析。Otsu適用於雜訊與目標像素值差距大時,若雜訊貼近目標像素值,Otsu方法的影像分割的前景則容易涵蓋雜訊,而G-Otsu適用於解決此問題。G-Otsu透過不同摘要統計量的應用,本篇共有M、V、MV、K和L版本的G-Otsu方法。K和L版本G-Otsu使用了不同的係數調整,結合了影像之間的相似性與變異性進行影像分割。K版本的G-Otsu在擁有5~100張影像的資料集中,成功分割影像的機率較原始Otsu版本高。透過G-Otsu,我們能夠更好地利用影像之間的特徵訊息。未來,我們可以優化G-Otsu,藉由額外的植物特徵或使用多通道分析,結合多閾值進行影像分析,擴大G-Otsu於其他作物的溫室或生長室的應用。同時,也將結合其他影像分析方法和機器學習方法,以進一步提升分析的準確性。期望基於植物影像特徵的分析將在農業領域發揮重要的作用,為作物生產的提升與農業發展做出貢獻。 | zh_TW |
dc.description.abstract | Image analysis is a powerful method with extensive applications in agriculture that can address various challenges effectively. Plant image analysis plays a crucial role in agriculture, enabling efficient and high-quality research based on plant phenotyping. The aim of this study is to explore algorithms based on plant features to conduct rapid non-destructive sampling for phenotypic trait analysis. The traditional Otsu algorithm is a single-channel image segmentation method. However, when noise pixel values are very close to the target pixel values, Otsu segmentation may inadvertently include noise. The G-Otsu algorithms are developed in this project, which is a multi-level model combining Otsu with additional information from multiple images. G-Otsu efficiently estimates multiple thresholds simultaneously for all images in the dataset. Five different versions of G-Otsu were tested and each employing different summary statistics: M, V, MV, K, and L versions. The K and L versions of G-Otsu employ distinct coefficient adjustments to incorporate both image similarity and variability into the segmentation algorithm. These two models show higher success rates in image segmentation compared to the original Otsu algorithm. In addition, the K-version of G-Otsu also demonstrates a higher success rate of image segmentation than Otsu in multiple datasets containing between 5 to 100 images. Through G-Otsu, we can better utilize feature information across images. In the future, we can further optimize G-Otsu by incorporating additional plant features or employing multi-channel analysis, integrating multiple thresholds for image segmentation, and expanding the application of G-Otsu to other crops in greenhouses or growth chambers. Additionally, combining other image analysis methods and machine learning techniques will further enhance the accuracy of analysis. It is expected that the analysis based on plant image features will play a crucial role in the agricultural field, contributing to crop production improvement and agricultural development. | en |
dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-08-15T17:13:31Z No. of bitstreams: 0 | en |
dc.description.provenance | Made available in DSpace on 2023-08-15T17:13:31Z (GMT). No. of bitstreams: 0 | en |
dc.description.tableofcontents | 論文口試委員審定書 i
中文摘要 ii Abstract iii 目錄 iv 表目錄 viii 圖目錄 ix 第1章 介紹 1 1.1 影像的基本構成 1 1.1.1 L*A*B*色彩空間 2 1.2 影像分析 2 1.2.1 影像分析的目的和用途 3 1.2.2 應用影像分析於植物與影像特徵 4 1.3 影像分析技術 4 1.3.1 影像分割-二值化 5 1.4 二值化方法-Otsu 6 1.4.1 優點與缺點 6 1.4.2 應用 6 1.5 多層次模型 7 1.6 研究目的 8 第2章 材料與方法 10 2.1 Otsu 10 2.2 G-Otsu 11 2.2.1 利用平均值建立G-Otsu 15 2.2.2 利用變異程度的概念建立G-Otsu 17 2.2.3 結合平均值與變異程度建立G-Otsu 19 2.2.4 調整平均值與變異程度的權重建立G-Otsu 20 2.2.4.1 調整平均值的權重建立G-Otsu 20 2.2.4.2 調整變異程度的權重建立G-Otsu 20 2.3 植物資料集 21 2.4 模擬資料 22 2.5 最佳化過程 23 2.6 實現方法 24 2.7 驗證資料 24 第3章 結果 26 3.1 基於平均值的G-Otsu 26 3.1.1 小的植物資料集 26 3.1.2 大的植物資料集 26 3.2 基於變異程度的G-Otsu 28 3.2.1 小的植物資料集 28 3.2.2 大的植物資料集 28 3.3 結合平均數與變異程度的G-Otsu 30 3.3.1 小的植物資料集 30 3.3.2 大的植物資料集 30 3.4 調整平均值與變異程度的權重建立G-Otsu 32 3.4.1 調整平均值的權重建立G-Otsu 32 3.4.1.1 小的植物資料集 32 3.4.1.2 大的植物資料集 34 3.4.2 調整變異程度的權重建立G-Otsu 36 3.4.2.1小的植物資料集 36 3.4.2.2 大的植物資料集 36 3.5 將K版本G-Otsu運用於不同大小資料集 39 第4章 討論 41 4.1 M版本G-Otsu應用於兩個植物資料集上 41 4.2 V版本G-Otsu應用於兩個植物資料集上 42 4.3 MV版本G-Otsu應用於兩個植物資料集上 42 4.4 調整MV版本G-Otsu應用於兩個植物資料集上 43 4.4.1 K版本G-Otsu應用於兩個植物資料集上 43 4.4.2 L版本G-Otsu應用於兩個植物資料集上 43 4.5 將K版本應用於G-Otsu運用於不同資料集 44 第5章 結論 45 5.1 多個版本的應用 45 5.2 G-Otsu與單一影像Otsu比較 46 5.3 未來方向 47 參考文獻 48 附錄 51 | - |
dc.language.iso | zh_TW | - |
dc.title | G-Otsu:分層模型應用於同時分割多個影像 | zh_TW |
dc.title | G-Otsu: Hierarchical Model for Simultaneous Segmentation of Multiple Images | en |
dc.type | Thesis | - |
dc.date.schoolyear | 111-2 | - |
dc.description.degree | 碩士 | - |
dc.contributor.coadvisor | 蔡政安 | zh_TW |
dc.contributor.coadvisor | CHEN-AN TSAI | en |
dc.contributor.oralexamcommittee | 劉力瑜;盧子彬 | zh_TW |
dc.contributor.oralexamcommittee | LI-YU LIU;TZU-PIN LU | en |
dc.subject.keyword | 植物影像,影像分割,Otsu,影像特徵,多層次模型, | zh_TW |
dc.subject.keyword | plant image,image segmentation,Otsu,image feature,hierarchical model, | en |
dc.relation.page | 62 | - |
dc.identifier.doi | 10.6342/NTU202302722 | - |
dc.rights.note | 未授權 | - |
dc.date.accepted | 2023-08-08 | - |
dc.contributor.author-college | 生物資源暨農學院 | - |
dc.contributor.author-dept | 農藝學系 | - |
顯示於系所單位: | 農藝學系 |
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