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研究生: 李蕙芳
論文名稱: 影像卡通化之研究
Image Cartoonlization
指導教授: 黃怡誠
Huang, Yi-Cheng
學位類別: 碩士
Master
系所名稱: 資訊教育研究所
Graduate Institute of Information and Computer Education
論文出版年: 2007
畢業學年度: 95
語文別: 中文
論文頁數: 93
中文關鍵詞: 平均移動演算法sobel邊緣偵測影像卡通化密度估計特徵空間
英文關鍵詞: Mean Shift Algorithm, Sobel Edge Detection, Image cartoonlization, Kernel Density Estimation, Feature Space Analysis, CIE Luv
論文種類: 學術論文
相關次數: 點閱:209下載:51
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  • 在這個3C產品盛行的世代裡,許多數位產品逐漸取代傳統產品,數位相機便是一例。隨著數位相機的普及與數位影像的技術的開發,使得人們能更輕鬆的記錄生活點滴。而影像處理軟體更是油然而生,提供大眾許多照片編修功能,然而照片編修已b不能夠滿足人們的需求,人們更希望藉由影像處理軟體所提供各種簡單且方便套用的特效來增加更多的生活樂趣。
    然而,許多知名的影像處理軟體中,在特效功能中卻鮮少具備將數位影像卡通化的功能;而線條簡單、色彩單調的卡通影像卻是深植於人們的生活中,不論是成人亦或是孩童均喜愛這類的圖像,因為卡通影像少了現實中複雜的線條與顏色,多了另一種韻味。
    因此,我們提供了一個系統,讓使用者能夠只須要輸入影像,系統便能將影像卡通化,透過不同參數的設定來達到不同的效果,為了達到此目標,我們合併了兩個演算法,分別是Mean Shift 演算法與Sobel演算法,透過對影像中的顏色做影像分割與區域合併,並以區域的平均顏色來著色,以減少影像中的色彩,並應用Sobel演算法,透過使用不同的門槛值來對影像做邊緣偵測,減少影像中的邊,讓影像卡通化,以期符合使用者的需求。

    The popularity of digital cameras and the advances of digital image processing technologies enable people to record their life story very conveniently. Image processing softwares provide many photo editing capabilities, but photo editing has been not meet people’s demands. People want to apply more special effect to photos to make more fun in the life.
    We found that a lot of well-know image processing software doesn't have the special effect of cartoonlizing an image which is perferable for many people. As a result, we development a system to enable users input images to our system and our system outputs the cartoon-like images.
    To achieve this goal, we combine two algorithms, mean shift algorithm and sobel edge detection. We use mean shift algorithm for dividing an image into contiguous regions of pixels and sobel edge detection for delineating the edges of an image. So users could use different parameters to make different effects.

    目錄 摘要…………………………………………………………………………………….I Abstract………………………………………………………………………………..II 圖目錄…………………………………………………………………………..…..VII 表目錄………………………………………………………………………………..XI 第一章緒論……………………………………………………………………………1 第一節 研究動機……………………………………………….……………….1 第二節 研究問題……………………………………….……………………….3 第三節 相關研究…………………………………….………………………….4 第四節 章節安排…………………………………….………………………….8 第二章 文獻探討……………………………………………………………………..9 第一節為不連續性分割,邊緣偵測……………………………………………10 2.1.1梯度運算子…………………………………………………………..11 2.1.2 Prewitt運算子……..……………………….………………………..12 2.1.3 Sobel 邊緣偵測法…………………………………………………..14 2.1.4 Laplacian 邊緣偵測法……………………...………………………15 第二節利用相似性分割,區域導向分割………………………………………17 2.2.1定義…………………………………………………………………..17 2.2.2 像素聚合的區域生長………………………………………………18 2.2.3 區域的分裂和合併…………………………………………………22 第三節為色彩系統…………………..…………………………………………24 第四節估計方法……………..…………………………………………………31 第三章 研究方法……………………………………………..….………………….35 3.1 Mean Shift Based segmentation…………………………………………….35 3.1.1特徵空間…………………………………….……………………………36 3.1.1.1 RGB 轉LUV………………………….………..………………………38 3.1.1.2 LUV 轉 RGB……………………..……………………………………40 3.1.2 Mean Shift …………………….………………………………………….42 3.2 Sobel Edge Detection……………….………………………………………50 第四章 實驗結果………………………………………..…………………………..51 第一節 程式介面………………………………………………………………52 第二節以設定不同的參數值進行實驗之結果……………………………….,57 4.2.1.固定hr為1與Minimum Region為0之mean shift 結果…………..58 4.2.2固定hr為2與Minimum Region為0之mean shift 結果…………59 4.2.3.固定hr為3與Minimum Region為0之mean shift 結果…………60 4.2.4固定hr為4與Minimum Region為0之mean shift 結果…………62 4.2.5.固定hr為5與Minimum Region為0之mean shift 結果…………65 4.2.6.固定hr為6與Minimum Region為0之mean shift 結果…………70 第三節為其他類型影像之實驗結果………………………..…………………75 第四節 實驗結果結論…………………………………………………………83 第五章 結論與未來展望………………………………………………...………….87 第一節 結論……………………………………………………………………87 第二節 未來展望………………………………………………………………88 參考文獻……………………………………………………………………..………89

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