本篇論文中提出了一個有效克服旋轉及縮放問題的混合型分類器，針對紋理影像經過放大或縮小後且經過裁切的影像進行辨識，此種影像常出現在照相機使用不同焦距來拍同一個景象時所出現的現象。這個混合分類器中包含了物件比對演算法及改良型賈波濾波器。物件比對演算法是以物件比對為基礎，找尋不同倍數的影像中相同內容的物件以進行物件相似性的匹配，並利用七個抗縮放的參數進行辨識。本篇論文所提出的另一種方法為改良型賈波濾波器，是根據賈波本身的多角度頻域優點，針對區域物件做訊號的轉換，並以區域物件的縮放比例，設定賈波頻率參數。此兩種分類器均採用SVM來當作分類模組，經實驗結果證明在抗旋轉及抗縮放辨識效果比其他方法來的好。

This thesis developed a hybrid classifier for correcting texture variation resulting from scale magnification, narrowing caused by cutting into the original size, or spatial rotation. These variations usually occur in images captured by a camera using different focal distances. The hybrid classifier contains an object matching algorithm and an improved Gabor filter. In object matching algorithm the classification between two textures is based on the comparisons of a set of similar objects which are extracted by using JSEG method. By adopting seven invariant scaled parameters the similar objects can be identified. Besides, an improved Gabor filter is proposed. In the improved Gabor filter the setting of scale parameter is based on the scale of object and the scanning region is located within the object. Under this modification the Gabor filter is more precise and more effective. Finally, SVMs are used as a classification model. Experimental results show that our proposed method outperforms existing design algorithms.

目錄
摘要I
AbstractII
誌謝III
目錄IV
表目錄VI
圖目錄VII
第一章、緒論1
1.1前言1
1.2研究動機與目的2
1.3論文架構4
第二章、知識背景5
2.1 二值化處理5
2.2 JSEG顏色切割6
2.2.1彩色空間量化8
2.2.2空間切割9
2.3物件連通法11
2.4灰階共生矩陣(GLCM)12
2.5 賈波濾波器15
2.6 支撐向量機器(Support vector machine)17
第三章、紋理影像縮放分析20
3.1紋理分析20
第四章、特徵攫取方法25
4.1物件比對演算法25
4.2改良型賈波演算法38
4.3物件比對演算法&改良型賈波合併40
第五章、實驗42
5.1紋理分類之效能評估42
第六章、結論53
參考文獻55

表目錄
表3.3.1 經灰度共生矩陣計算特徵表22
表3.3.2 物件特徵值24
表5.1.1 抗旋轉與其他方法之比較(其輸出值為百分比%)45
表5.1.2 抗縮放與其他方法之比較(其輸出值為百分比%)49

圖目錄
圖2.1.1 不同對比強度的圖形二值化比較6
圖2.2.1 不同J值範例.7
圖2.2.2 JSEG流程圖10
圖2.2.3 使用JSEG切割演算法結果11
圖2.3.1 物件連通法12
圖2.4.1 3×3行列標號視窗13
圖2.4.2 紋理影像共生矩陣14
圖2.4.3 PH、PV、PLD、PRD共生矩陣14
圖2.5.1 賈波角度紋理訊息15
圖2.6.1 ζ i變數(slack variables)示意圖19
圖3.3.1 放大後取相同尺寸之圖形21
圖3.4.1 特徵物件23
圖4.1.1 物件比對演算法流程圖26
圖4.1.2 三類顏色圖27
圖4.1.3 物件重心28
圖4.1.4 最小面積橢圓找尋結果33
圖4.1.5 橢圓內灰階值特徵計算33
圖4.1.6 最佳區域34
圖4.1.7 影像物件代表圖35
圖4.1.8 物件比對演算法詳細流程36
圖4.1.9 圖形放大後攫取的最佳特徵物件37
圖4.2.1 改良型賈波流程圖39
圖4.3.1 結合物件比對演算法和改良型賈波演算法流程41
圖5.1.1 Brodatz texture紋理資料庫中的32種紋理影像42
圖5.1.2 Brodatz texture紋理資料庫中的74種紋理影像43

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