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研究生： 陳柏翰
Bo-Han Chen
論文名稱： 植基於支持向量機之快速彩色濾波陣列樣型辨識方法
Fast SVM-based identification of arbitrary CFA images
指導教授： 鍾國亮
Kuo-Liang Chung
口試委員: 廖弘源
Mark Liao
范國清
Kuo-Chin Fan
貝蘇章
Soo-Chang Pei
徐繼聖
Gee-Sern Hsu
學位類別： 碩士
Master
系所名稱： 電資學院 - 資訊工程系
Department of Computer Science and Information Engineering
論文出版年： 2016
畢業學年度： 104
語文別： 中文
論文頁數： 30
中文關鍵詞： 機器學習 、支持向量機 、交叉驗證 、馬賽克影像 、彩色濾波陣列
外文關鍵詞： Machine learning, Support vector machine, Cross-validation, Mosaic image, Color Filter Array
相關次數： 點閱：191  下載：2 
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    • 馬賽克影像中，每一個通過彩色濾波陣列的像素點僅由一個顏色所組成，遺
    失彩色濾波陣列將無法解馬賽克。本篇論文提出一套植基於支持向量機的彩色濾
    波陣列影像辨識方法。本篇論文所提出方法能辨識十一種彩色濾波陣列，方法分
    為兩階段。第一階段藉由在空間域抽取彩色濾波陣列特徵訓練與預測支持向量機，
    能直接辨識六種彩色濾波陣列，其餘的五種因為有誤判發生，需要在第二階段進
    行辨識。第二階段使用決定樹能夠正確辨識其餘五種彩色濾波陣列。在本實驗中，
    我們實五組作交叉驗證，實驗結果顯示和先前方法相比，本篇論文所提植基於支
    持向量機之二階段辨識方法與先前方法相比，能辨識在更短的時間內辨識彩色濾
    波陣列種類。

    Considering mosaic images, each pixel captured by color filter array is composed
    of only one primary color. Without the color filter array (CFA) pattern information, it
    is hard to demosaic or compress CFA images. In this paper, we propose a SVM based
    method to identify the CFA pattern of the input CFA image. The proposed method has
    two stages. In the first stage, we train SVM by features extracted from the spatial
    domain of CFAs. In this stage, 6 CFA structures can be recognized and the other 5 CFA
    structures can be identified in the second stage. In the second stage, we use decision
    tree approach to identify the remaining CFA structures. Based on 5 groups crossvalidation, experimental results demonstrate that the proposed SVM based
    identification method can identify CFA structures faster when compared with the stateof-the-art algorithm.

    中文摘要.........................................................................................................................I Abstract..........................................................................................................................II 銘謝..............................................................................................................................III 目錄..............................................................................................................................IV 圖目錄...........................................................................................................................V 表目錄..........................................................................................................................VI 第一章 緒論..................................................................................................................1 第二章 相關結果介紹:Huang et al.的方法..................................................................5 第三章 植基於支持向量機的快速辨識彩色濾波陣列方法......................................6 3-1 彩色濾波陣列結構的特性以及所提方法的假設.................................................8 3-2 在空間域抽取特徵之方法....................................................................................10 3-3 支持向量機的訓練與預測....................................................................................12 3-4 誤判補償的二階段辨識........................................................................................14 第四章 實驗結果........................................................................................................19 第五章 結論與未來工作............................................................................................29 5.1 結論.......................................................................................................................29 5.2 未來工作...............................................................................................................29 參考文獻......................................................................................................................30

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