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研究生: 鄭朝允
論文名稱: 常見食用性貝類辨識之研究
The identification of common edible shellfishs
指導教授: 葉榮木
Yeh, Zong-Mu
蔡俊明
Tsai, Chun-Ming
學位類別: 碩士
Master
系所名稱: 機電工程學系
Department of Mechatronic Engineering
論文出版年: 2013
畢業學年度: 101
語文別: 中文
論文頁數: 113
中文關鍵詞: 主成分分析統計分析傅立葉轉換小波轉換
英文關鍵詞: PCA, statistical analysis, Fourier transform, wavelet transform
論文種類: 學術論文
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  • 民以食為天。相信大家都有吃過熱炒或海鮮的經驗,尤其文蛤更是常見的海鮮料理食材,而它們的貝殼有些人喜歡把玩;有些人喜歡收藏。由於一般人對於貝類的認知不是很清楚,若從書上或是網路上比對資料那是相當曠日廢時,故能發展一套系統能夠準確的辨識,不僅可以快速查詢貝殼的種類,也可以減少人力的辨識。本研究針對數位典藏與數位學習成果入口網中的食用貝類進行研究辨識,共44種。
    本研究最好的結果為實驗E,其方法首先為輸入貝類影像;其次,將影像轉為灰階圖;第三,對灰階影像做快速傅立葉轉換;第四,選取在四角之低頻頻率,其大小為 矩陣;最後,利用SVM分類即可辨識出為哪種貝類,其準確率有到100%,平均辨識一張貝類影像所花的時間約為0.044秒。

    Food is the first necessity of the people. I believe that everyone has the experience of eating stir-fry or seafood, especially clam as a common ingredient of seafood cuisine. Some people like playing with its shell, while some others like collecting. Ordinary people generally don’t know much about shellfish, and it takes too much time to find information from books or the internet. Therefore, a perfect system for accurate identification can not only provide quick query of shell kinds, but can also reduce manpower in identification. This study focused on the research and identification of 44 kinds of edible shellfish recorded on the website of Digital Taiwan – Culture & Nature.
    The best result of this study was experiment E: first, input the image of shellfish; second, convert the image into grayscale image; third, perform Fast Fourier Transform with the grayscale image; fourth, choose the low frequencies at four corners, which were 7×7 matrixes; finally, get the result of shellfish identification through SVM classification. The accuracy could reach 100% and the average time spent on identifying a shellfish image was about 0.044s.

    致謝 i 摘要 ii Abstract iii 目錄 iv 圖目錄 vii 表目錄 xii 第一章 緒論 1 1.1前言 1 1.2研究背景 1 1.3研究動機與目的 3 1.4論文架構 4 第二章 文獻探討 5 2.1 國外影像檢索系統 5 2.1.1 VisualSEEK 5 2.1.2 Photobook 6 2.1.3 QBIC 8 2.1.4 WebSEEk 10 2.2 國內影像檢索系統 11 2.3 其他貝類辨識文獻 15 第三章 相關研究理論 17 3.1 彩色空間 17 3.1.1 RGB彩色空間 17 3.1.2 YCbCr彩色空間 19 3.1.3 HSV彩色空間 21 3.1.4 HSL彩色空間 23 3.1.5 YIQ彩色空間 24 3.1.6 CMYK彩色空間 26 3.2 邊緣偵測 28 3.2.1 Laplace邊緣偵測 28 3.2.2 Sobel邊緣偵測 30 3.2.3 Prewitt邊緣偵測 31 3.2.4 Roberts邊緣偵測 33 3.2.5 Canny邊緣偵測 34 3.3主成分分析(Principal Component Analysis, PCA) 38 3.4支持向量機 (Support vector machine, SVM ) 41 3.5統計特徵 42 3.5.1直方圖 42 3.5.2統計量測 44 3.6傅立葉分析 45 3.6.1傅立葉轉換(Fourier Transform) 45 3.6.2短時間傅立葉轉換(Short-Time Fourier Transform,STFT) 46 3.6.3離散時間傅立葉轉換(Discrete-Time Fourier Transform, DTFT) 47 3.6.4離散傅立葉轉換(Discrete Fourier Transform, DFT) 48 3.6.5快速傅立葉轉換(Fast Fourier Transform, FFT) 49 3.7小波分析 50 3.7.1小波轉換(Wavelet Transform) 50 3.7.2小波函數(Wavelet Function) 51 3.7.3連續小波轉換(Continuous Wavelet Transform, CWT) 52 3.7.4離散小波轉換(Discrete Wavelet Transform, DWT) 55 3.7.5多層解析分析(Multiresolution Analysis, MRA) 56 3.7.6哈爾小波轉換(Haar Wavelet Transform) 57 3.7.7影像哈爾小波轉換 59 3.7.8小波家族(Wavelet Families) 62 3.7.9小波與傅立葉轉換整理比較 64 第四章 研究方法與結果 65 4.1實驗A:主成分分析實驗結果 66 4.2實驗B:小波轉換頻率特徵實驗結果 80 4.3實驗C:統計特徵實驗結果 88 4.4實驗D:小波轉換頻率特徵及統計特徵實驗結果 76 4.5實驗E:傅立葉轉換頻率特徵實驗結果 92 4.6實驗F:傅立葉轉換頻率特徵及統計特徵實驗結果 85 4.7各實驗整理 95 第五章 討論與結論 96 第六章 未來研究 99 參考文獻 100 附錄A libsvm使用方法 105 附錄B weka使用方法 109

    英文部分:
    [1] M.S. Lew, N. Sebe, C. Djeraba and R. Jain, “Content-based Multimedia Information Retrieval:State of the Art and Challenges”, ACM Transactions on Multimedia Computing, Communications, and Applications, pp. 1–19, 2006.
    [2] J. Eakins and M. Graham, “Content-based Image Retrieval”, JISC Technology Applications Program, 1999
    [3] A.D. Bimbo and P. Pala, “Visual Image Retrieval by Elastic Matching of User Sketches,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 2, 1997
    [4] VisualSEEk, http://www.aa-lab.cs.uu.nl/cbirsurvey/cbir-survey/node42.html
    [5] PhotoBook, http://vismod.media.mit.edu/vismod/demos/photobook/ foureyes/ph-figs/foureyes.jpg
    [6] PhotoBook, http://www.aa-lab.cs.uu.nl/cbirsurvey/cbir-survey/node30.html
    [7] The QBIC Project in the Department of Art and Art History at UC Davis, http://asis.org/annual-97/holt.htm
    [8] WebSEEk, http://www.aa-lab.cs.uu.nl/cbirsurvey/cbir-survey/node44.html
    [10] Color Theory and Principles, http://www.infocellar.com/graphics/color-theory.htm
    [11] Color Spaces, http://www.couleur.org/index.php?page=transformations
    [12] HSV cylinder color solid comparison, https://zh.wikipedia.org/wiki/ File:HSL_HSV_cylinder_color_solid_comparison.png
    [13] SubtractiveColor, http://en.wikipedia.org/wiki/File:SubtractiveColor.svg

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    [22] J.C. Platt, N. Cristianini, and J. Shawe-Taylor,:” Large margin DAGs for multiclass classification,” Advances in Neural Information Processing Systems, Vol. 12, pp. 547-553, 2000.
    [23] C. Cortes and V. Vapnik, “Support Vector Network,” Machine Learning, vol. 20, pp. 273-297, 1995.
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    [26] S.R. Gunn, “Support Vector Machines for Classification and Regression,” Technical Report, 329 University of Southampton, 1998.

    [28] C.W. Hsu, and C.J. Lin, “A Comparison of Methods for Multiclass Support Vector Machines,” IEEE Transactions on Neural Networks, vol. 13, no. 2, pp. 415-425, 2002.
    [29] C.W. Hsu, C.C. Chang, and C.J. Lin, “A Practical Guide to Support Vector Classification,” Department of Computer Science and Information Engineering, National Taiwan University, 2003.
    [30] R. Kimmel and A.M. Bruckstein, “Regularized Laplacian Zero Crossings as Optimal Edge Integrators” , International Journal of Computer Vision 53(3), pp.225–243, 2003.
    [31] J. Canny, “A Computational Approach to Edge Detection,” IEEE Transactions on Pattern Analysis and Machine intelligence, Vol. PAMI-8, No.6, November 1986.
    [32] S. Qiang and L. Liu, “Compare between several Linear Image Edge Detection Algorithm”, Second International Conference on Machine Vision, pp. 259-263, 2009.
    [33] Wavelet Toolbox For Use with MATLAB, http://web.mit.edu/1.130/WebDocs/wavelet_ug.pdf
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    [35] Wavelet Analysis, http://radio.feld.cvut.cz/matlab/toolbox/wavelet/ch01_in8.html
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    [37] Y. Meyer, “Ondelettes et Functions splines,” in Seminaire EDP, Ecole Polytechnique, Paris, Frances, Dec. 1986.
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    中文部份:
    [9] 蘇裕盛,「利用影像特徵選取及分類方法於貝類檢索之研究」,國立高雄第一科技大學資訊管理系碩士論文,2008。
    [14] 鐘國亮,「影像處理與電腦視覺」,台灣東華書局股份有限公司,2004
    [15] Gonzalez.Woods,Digital Image Processing 3/e,繆紹剛譯,「數位影像處理」,台灣培生教育出版股份有限公司,2009
    [24] 楊棠鈞,“結合Adaboost 分類器和支援向量機的路標辨識系統之實現”,國立成功大學工程科學系碩士論文,2009。
    [27] 林維謙,“植基於支援向量機之人臉偵測與人臉辨識”,世新大學管理學院碩士論文,2007。
    [38] 賴新田,“協助國小學生認識校園植物系統之研製”,臺北市立教育大學數學資訊教育教學碩士學位班碩士論文,2012。
    [41] http://catalog.digitalarchives.tw/item/00/11/28/bb.html
    [42] http://catalog.digitalarchives.tw/item/00/07/fc/03.html
    [43] http://turing.csie.ntu.edu.tw/ncnudlm/
    [44] http://turing.csie.ntu.edu.tw/ncnudlm/query_visual_bi/index.htm
    [45] http://turing.csie.ntu.edu.tw/ncnudlm/query_keyword/q_by_word.htm
    [46] http://fishdb.sinica.edu.tw/
    [47] http://fishdb.sinica.edu.tw/chi/home.php
    [48] http://shell.sinica.edu.tw/chinese/index_c.php
    [49] http://shell.sinica.edu.tw/chinese/shelloutline.php?PAGE=1
    [50] http://shell.sinica.edu.tw/chinese/classification_T.php
    [51] http://catalog.digitalarchives.tw/Hotkey/428/1.html

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