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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 黃乾綱 | |
dc.contributor.author | Cheng-Kai Chang | en |
dc.contributor.author | 張証凱 | zh_TW |
dc.date.accessioned | 2021-06-16T13:03:50Z | - |
dc.date.available | 2013-08-09 | |
dc.date.copyright | 2013-08-09 | |
dc.date.issued | 2013 | |
dc.date.submitted | 2013-08-05 | |
dc.identifier.citation | 1. 陳瑞良, 自動計算癌細胞群落數之影像分析系統. 2009.
2. Men, H., et al. Application of support vector machine to heterotrophic bacteria colony recognition. 2008. IEEE. 3. Men, H., et al. Counting method of heterotrophic bacteria based on image processing. in Cybernetics and Intelligent Systems, 2008 IEEE Conference on. 2008. IEEE. 4. Wei-zheng, S., et al. Experimental study for automatic colony counting system based on image processing. 2010. IEEE. 5. Cortes, C. and V. Vapnik, Support-vector networks. Machine learning, 1995. 20(3): p. 273-297. 6. Serra, J., Image analysis and mathematical morphology1982: London.: Academic Press.[Review by Fensen, EB in: J. Microsc. 1311983) 258.] Review article General article, Technique Microscopy Staining, Mathematics, Cell sizePMBD, 185707888). 7. Vincent, L. and P. Soille, Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE transactions on pattern analysis and machine intelligence, 1991. 13(6): p. 583-598. 8. MacQueen, J. Some methods for classification and analysis of multivariate observations. in Proceedings of the fifth Berkeley symposium on mathematical statistics and probability. 1967. California, USA. 9. Chen, W.-B. and C. Zhang, An automated bacterial colony counting and classification system. Information Systems Frontiers, 2009. 11(4): p. 349-368. 10. Ray, S. and R.H. Turi. Determination of number of clusters in k-means clustering and application in colour image segmentation. in Proceedings of the 4th international conference on advances in pattern recognition and digital techniques. 1999. 11. Suzuki, K., I. Horiba, and N. Sugie, Linear-time connected-component labeling based on sequential local operations. Computer Vision and Image Understanding, 2003. 89(1): p. 1-23. 12. Canny, J., A computational approach to edge detection. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 1986(6): p. 679-698. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/61476 | - |
dc.description.abstract | 菌落計數是微生物實驗的基礎及必要的工作,然而在實驗的過程中,最令實驗者困擾的地方在於培養實驗結束後,必需估算為數眾多培養皿內的菌落數,這一個非常重要但是相當耗費人力時間的步驟。為了解決上述問題,市面上已經有商業化的菌落自動計數機器,但是這些儀器價格上十分昂貴,使用上無法任意搬移到特定地區。隨著智慧型手機的普及率漸漸提升,本研究開發一款手機APP來解決上述問題,APP透過手機的攝影鏡頭擷取培養皿內的菌落數影像,再利用影像分析技術與機器學習方法技術,統計出菌落的數量。本研究特別針對智慧型手機APP設計演算法,避免在處理計算菌落的時候發生記憶體不足的情況。相較於其他相關研究所提出的方法,本研究方法不僅快速、準確率達97%且便利,更可以解決更多種不同顏色的菌落與更多不同顏色的背景的培養皿圖像。同時本研究也提供了網路服務,使用者可以利用APP上傳培養皿圖片到此服務進行大量且快速的自動菌落計數。 | zh_TW |
dc.description.abstract | Microbiological experiment is the basis of biotechnology. Colony counting is a very important step after experiments. However colony counting is a time-consuming and inefficient process in the microbiological experiments. In this thesis, with the population of smartphone, we proposed an approach and developed a mobile application, which utilize the mobility of smartphone to help user count the number of colonies. After taking photos of petri dish, machine learning and image processing are used to count the number of colonies. Compare with previous work, our approach overcome the difficulties of processing various type of colonies and culture medium. We also propose a new approach which prevent the out of memory situation while the smartphone is processing the colony images. Each pixel in the image of colony is viewed as a data. The data is going to cluster with K-means algorithm. Thereafter three features are extracted including area, perimeter and shape factor. These features are used to identify colonies. We also provide an on-line service to which user can use the APP to upload images of petri dish for mass and rapid automatic colony counting. Our approach is not only fast and convenient but also capable to deal with various type of colony with various backgrounds with the precision rate 97%. | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T13:03:50Z (GMT). No. of bitstreams: 1 ntu-102-R00525041-1.pdf: 5727044 bytes, checksum: e10578e62c844fea51d3502233a53be2 (MD5) Previous issue date: 2013 | en |
dc.description.tableofcontents | 口試委員會審定書 #
誌謝 i 中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF TABLES xii Chapter 1 緒論 1 1.1 研究背景 1 1.1.1 智慧型手機普及 1 1.1.2 微生物實驗 3 1.2 研究動機 4 1.3 研究目的 5 1.4 研究貢獻 5 1.5 論文架構 6 Chapter 2 文獻探討 7 2.1 相關研究 7 2.1.1 疊代二分法 7 2.1.2 權重疊代二分法 8 2.1.3 支持向量機實作菌落辨識 9 2.2 影像處理 10 2.2.1 形態學 (Morphology) 處理 10 2.2.2 分水嶺切割法 11 2.3 資料分群 12 Chapter 3 研究方法 14 3.1 原始影像的輸入與前處理 16 3.2 二階段資料分群 17 3.3 形態學處理與辨識菌落 23 3.4 面積百分比菌落數量估算法 26 3.5 三種類分群菌落估算法 29 Chapter 4 實驗結果與討論 36 4.1 實驗環境 36 4.2 結果與討論 36 4.3 結論與未來展望 44 4.3.1 結論 44 4.3.2 未來展望 45 參考文獻 46 附錄1 47 附錄2 48 附錄3 55 | |
dc.language.iso | zh-TW | |
dc.title | 基於智慧型手機自動菌落計數系統 | zh_TW |
dc.title | Mobile Phone Based Bacteria Colony Counting | en |
dc.type | Thesis | |
dc.date.schoolyear | 101-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 張瑞益,張恆華,鄭卜任,陳明汝 | |
dc.subject.keyword | 菌落計數, | zh_TW |
dc.subject.keyword | Colony count, | en |
dc.relation.page | 56 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2013-08-05 | |
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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