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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 黃乾綱(Chien-Kang Huang) | |
dc.contributor.author | Wan-Jen Lu | en |
dc.contributor.author | 盧琬臻 | zh_TW |
dc.date.accessioned | 2021-06-16T09:20:48Z | - |
dc.date.available | 2019-07-07 | |
dc.date.copyright | 2017-07-07 | |
dc.date.issued | 2017 | |
dc.date.submitted | 2017-06-30 | |
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Hannuna, and N. Canagarajah, Automatic Leaf Extraction from Outdoor Images. 20. Tang, X., et al. Leaf extraction from complicated background. in Image and Signal Processing, 2009. CISP'09. 2nd International Congress on. 2009. IEEE. 21. Zhu, H.-D., D. Wu, and Z. Sun. Plant Leaves Extraction Method Under Complex Background Based on Closed-Form Matting Algorithm. in International Conference on Intelligent Computing. 2015. Springer. 22. Fu, H. and Z. Chi. A two-stage approach for leaf vein extraction. in Neural Networks and Signal Processing, 2003. Proceedings of the 2003 International Conference on. 2003. IEEE. 23. Cope, J.S., et al., Plant species identification using digital morphometrics: A review. Expert Systems with Applications, 2012. 39(8): p. 7562-7573. 24. Wu, S.G., et al. A leaf recognition algorithm for plant classification using probabilistic neural network. in Signal Processing and Information Technology, 2007 IEEE International Symposium on. 2007. IEEE. 25. 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[cited 2017 May 20]; Available from: http://www.hla.hlc.edu.tw/hlawww/dept04/hla_greenmap/tree_identifying.htm. 49. Forest Trees of Texas - How to Know Them. 2016 [cited 2017 May 20]; Available from: http://www.gutenberg.org/files/52651/52651-h/52651-h.htm. 50. 使用代码将照片变成卡通图片. [cited 2017 May 20]; Available from: https://zhuanlan.zhihu.com/p/25556276. 51. Du, J.-X., et al., Computer-aided plant species identification (CAPSI) based on leaf shape matching technique. Transactions of the Institute of Measurement and Control, 2006. 28(3): p. 275-284. 52. Gwo, C.-Y., C.-H. Wei, and Y. Li, Rotary matching of edge features for leaf recognition. Computers and electronics in agriculture, 2013. 91: p. 124-134. 53. Lukic, M., E. Tuba, and M. Tuba. Leaf recognition algorithm using support vector machine with Hu moments and local binary patterns. in Applied Machine Intelligence and Informatics (SAMI), 2017 IEEE 15th International Symposium on. 2017. IEEE. 54. Priya, C.A., T. Balasaravanan, and A.S. Thanamani. An efficient leaf recognition algorithm for plant classification using support vector machine. in Pattern Recognition, Informatics and Medical Engineering (PRIME), 2012 International Conference on. 2012. IEEE. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/59335 | - |
dc.description.abstract | 地球上各種樹木植物是最重要的自然資源之一,提供人類棲所與光合作用產生氧氣,全世界的植物物種有成千上萬種,該怎麼分類透過植物影像分類物種、依照外觀特徵辨識該物種是個很重要的議題,未來若能透過藉由行動裝置拍攝的植物影像達到物種辨識的效果,將會是個很方便又快速的途徑去認識植物。
以最常見且一年四季皆可取得的植物葉片而言,葉片上包含了許多不同的特徵,例如葉片邊緣鋸齒、葉脈走向、葉尖與葉基形狀、葉片長寬比例、葉片顏色、葉片表面質地與材質等等,可以藉由不同的葉片特徵來辨別植物物種。以往的研究方法使用一組葉片特徵值組合來描述所有影像中的植物葉片,但並非每項特徵在所有植物葉片上皆是重要特徵。有鑒於舊方法不能完整並精確描述各物種葉片,故本研究提出全新的兩階段分類方法,第一階段分類用以分類葉片形狀,藉由觀察所有常見的葉片形狀,將植物葉片依照形狀區分四大類形狀:長條形、掌形、三角與心形、圓與橢圓形,此四種類別外觀差異最大。第二階段的分類是植物物種辨識,於每一形狀類別各自使用不盡相同的特徵值以區分植物物種,除了採用葉形特徵之外,還考慮了葉片鋸齒、葉片表面反光程度等能描述更多葉片細節的特徵值。 實驗證明針對不同形狀的葉形類別使用了不同組合的特徵值進行影像辨識,讓每個形狀類別皆能使用到最合適的特徵值,確實有效的降低辨識錯誤率。從研究結果可以確實觀察出圓與橢圓形的葉片形狀有較高的辨識難度,相對的其他三種類的葉片形狀分類錯誤率極低,整體植物物種辨識錯誤率僅 6.93%。於文章末討論辨識錯誤的葉片中造成辨識效果不佳的原因並討論解法。 | zh_TW |
dc.description.abstract | A variety of plant species on the earth is one of the most important natural resources, plants could be a shelter for human and do photosynthesis to produce oxygen, there are thousands of plant species around the world, how to classify so many species by leaf image? Using the appearance, flower, and leaf of a plant is a very important issue. If we can use the plant images taken by a mobile device to identify plant species in the future, it will be a convenient and quick way to know plants.
Leaf is the most common organ of a plant, it can be obtained all year, the leaf contain many different features, such as leaf edge, veins, shape, leaf aspect ratio, leaf color and leaf surface texture, etc., people can identify a wide variety of plant species by there features. Previous studies used a set of leaf features to describe the leaf of all plant, but not every feature was an important feature on all plant. So this study propose a whole new approach by operate a two-stage classification. The first stage is a leaf shape classification. Observing all the common shape of the blade, there are four distinguish shape class: long, palmate, triangle and heart, round and oval. The second stage of classification is the recognition of plant species. In addition to the leaf shape features, the leaf sawtooth, the reflection degree on the leaf surface, the features which can describe the details are used to identify plant species. In each shape class, the different features are applied to different shape class. This study shows that each shape class use the most suitable combination of features will exactly has lower error rate of plant species recognition. It can be observed from the results that the round and oval shape class has a higher identification difficulty, while the other three classes of leaf shape has a very low error rate of classification. the overall of species recognition, error rate is only 6.93%. In the end of this study, we discuss the reasons for the wrong recognition. | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T09:20:48Z (GMT). No. of bitstreams: 1 ntu-106-R04525054-1.pdf: 45159096 bytes, checksum: 18539aa299ddd5f19c3f547250db50a2 (MD5) Previous issue date: 2017 | en |
dc.description.tableofcontents | 口試委員審定書 i
致謝 iii 中文摘要 v ABSTRACT vii 目錄 ix 圖目錄 xiii 表目錄 xvii 第一章 緒論 1 1.1 淺談植物資源與自動辨識系統 1 1.2 自動辨識植物的目的 2 1.3 研究貢獻 2 1.4 論文架構 3 第二章 文獻探討 5 2.1 葉片特徵 6 2.2 影像預處理 9 2.2.1 影像平滑濾波器 10 2.2.2 二值化方法 13 2.3 HSV 與 RGB 色彩空間分析與比較 15 2.3 分類器 17 2.3.1 Support Vector Machine(SVM) 17 2.3.2 k-Nearest Neighbors Algorithm(k-NN) 18 2.3.3 Decision Tree 與 Random Forest 19 2.4 葉片影像資料庫 19 2.4.1 Flavia Dataset 19 2.4.2 Swedish Leaf Dataset 20 2.4.3 Leafsnap Dataset 21 2.4.4 Pl@ntNet 22 2.5 問題定義 23 第三章 研究方法 25 3.1 基礎理論 25 3.2 研究架構與流程 27 3.3 植物葉片各部位名稱 28 3.4 影像預處理與擷取葉片特徵方法 29 3.5 葉片特徵值 33 3.3.1 實際測量值 33 3.3.2 葉片特徵值 38 3.6 兩階段分類與特徵值的選用 44 3.6.1 葉片形狀分類 44 3.6.2 植物物種分類 45 第四章 研究結果與分析 47 4.1 實驗影像與自動辨識系統 47 4.2 研究結果與討論 48 4.2.1 第一階段葉片形狀分類成果 48 4.2.2 討論本研究新提出的特徵值用於植物物種分類階段 49 4.2.3 第二階段植物物種分類成果 52 4.2.4 討論分類錯誤的狀況 56 4.3 一階段分類與兩階段分類比較 61 第五章 結論與未來展望 63 5.1 結論 63 5.2 未來展望 63 參考文獻 65 附錄 A Flavia Dataset 32 植物物種學名、俗名與影像數量 71 附錄 B Flavia Dataset 32 植物物種影像葉片特徵值數值範圍 73 附錄 C Flavia Dataset 32 植物物種每物種使用 35 張訓練資料影像與 10 張測試資料影像實驗成果 87 附錄 D SVM 分類器由 Grid Search 尋找最佳參數結果 93 | |
dc.language.iso | zh-TW | |
dc.title | 基於葉片影像特徵的植物物種自動辨識研究 | zh_TW |
dc.title | Research on Plant Species Automatic Identification via Leaf Image Recognition | en |
dc.type | Thesis | |
dc.date.schoolyear | 105-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 劉力瑜,張恆華 | |
dc.subject.keyword | 植物物種辨識,特徵值擷取,影像處理,植物葉片分析,分類, | zh_TW |
dc.subject.keyword | plant recognition,feature extraction,Image processing,leaf shape analysis,classification, | en |
dc.relation.page | 96 | |
dc.identifier.doi | 10.6342/NTU201701219 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2017-07-02 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 工程科學及海洋工程學研究所 | zh_TW |
顯示於系所單位: | 工程科學及海洋工程學系 |
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