近年來，如何發展出一套自動且人性化的視覺檢測系統，一直受到許多相關研究團體的探討和重視，例如：工業檢測、指紋辨識、醫療檢測和遙測技術等領域，都有影像處理相關的研究與應用。而影像分類可以視為是訓練電腦如何來區分影像的一種方法，讓電腦了解人類的思考模式，以及判斷方法，其衍生出來的應用可以帶給人們相當大的便利。
影像分類的相關技術通常會以影像的相關特徵值作為分析的重點，而其中紋理特徵值是最可以清楚描述出影像中的物體類別的特徵之一，所以以紋理特徵值做為分類之依據，將可明確的描述出影像所屬的類別。本研究主要探討關於紋理影像分類，首先利用灰階共生矩陣(Gray-Level Co-occurrence Matrix；GLCM)將紋理影像做轉換，之後擷取出二種統計特徵值及七種灰階共生矩陣特徵值，再將所擷取出的紋理特徵值利用支向機(Support vector machines；SVM)以及模糊支向機(Fuzzy Support vector machines；FSVM)進行影像分類之步驟，最後比較傳統支向機與模糊支向機的分類結果，根據所得之結果顯示，兩個分類器之間的分類正確率幾乎是相同的。

In recent years, to develop an automatic and user-friendly vision inspection system has been explored by a number of related researchers and groups, such as: industrial inspection, fingerprint identification, medical testing and remote sensing technology. There are image processing technologies and applications in the above fields. Images classification can be regarded as one technology that trains computer to identify and distinguish among images, understand human thinking and decision model. Its future application derived from it can bring the considerable convenience.
Image classification techniques are usually related to image analysis of the relevant characteristic values. Texture feature attribute is one of the clearest descriptions of the image of object categories. Therefore, texture feature attributes as a basis for classification will be a clear description of different type of texture image. This study focused on the on the texture image classification. First, author uses gray-level Co-occurrence matrix (GLCM) of texture images to transfer the original texture images. After capturing of two kinds of eigenvalue statistics and seven gray-level Co-occurrence matrix eigenvalue, then author classifies the texture features using support vector machines (SVM) and fuzzy support vector machines (FSVM) for image classification. Finally, comparison with traditional support vector machines and fuzzy support vector machines classification of the obtained results, it is showed that the results of two classifiers are almost the same.

中文摘要 i
英文摘要 ii
誌謝 iii
目錄 iv
表目錄 vi
圖目錄 vii
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 1
1.3 研究目的 2
1.4 研究架構與流程 2
第二章 文獻探討 4
2.1 紋理分析 4
2.2 支向機 7
2.3 模糊支向機 8
第三章 研究方法 11
3.1 實驗流程 11
3.2 灰階共生矩陣(Gray-Level Co-occurrence Matrix；GLCM) 12
3.3 紋理特徵擷取(Features extraction) 15
3.4 支向機(Support Vector Machine；SVM)分類 17
3.4.1 線性可分支向機 17
3.4.2 線性不可分支向機 19
3.4.3 非線性可分支向機 20
3.5 模糊支向機(Fuzzy Support Vector Machine；FSVM)分類 21
3.5.1 不同的權重於兩個類別 22
3.5.2 Fuzzy membership的計算 23
3.5.3 Kernel函數 25
3.6 多類支向機 25
3.6.1 Holdout Method 26
3.6.2 Cross-Validation Method 26
3.6.3 One-against-all method 27
第四章 實驗結果與分析 28
4.1資料來源與格式 28
4.2影像前處理及特徵值擷取 29
4.3 支向機分類結果 30
4.4 模糊支向機分類結果 33
4.5 SVM與FSVM分類之結果比較 36
第五章 結論與後續建議 37
5.1 結論 37
5.2 後續建議 38
參考文獻 39
附錄A FSVMs SMO之pseudo-code 43
附錄B 訓練資料特徵值之數據 46

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