臺灣博碩士論文加值系統

本論文所提出智慧型彩色影像紋理辨識技術，首先將彩色影像轉換為R、G、B三張影像，再將所獲得的三張影像利用奇異值分解加強影像之紋理並結合離散小波轉換特徵抽取方法抽取紋理色彩特徵，接著使用支持向量機及粒子族群最佳化演算法挑選最佳特徵組合與支持向量機參數。根據實驗結果顯示，本研究所提出之方法其效能優於其他現存的影像辨識方法。

This paper presents a novel intelligent color image texture recognition technique which is called the ICITR technique. First, it applies singular value decomposition (SVD) to each channel image of a RGB color image to enhance image textures. Subsequently, it extracts color texture features in discrete wavelet transform domain of the original channel image and its corresponding enhanced texture image with SVD. The ICITR technique employs a support vector machine (SVM) to be a multiclassifier. Meanwhile, particle swarm optimization is utilized to select the best feature combination and the parameters used in the SVM. The experimental results show that the ICITR technique can achieve satisfying results and also outperforms other existing methods under consideration here.

目錄
中文摘要 ...............i
英文摘要 ...............ii
誌謝 ...............iii
目錄 ...............iv
表目錄 ...............vi
圖目錄 ...............vii
一、 緒論 ...............1
1.1 研究背景............... 1
1.2 相關研究 ...............3
1.3 研究動機與目的...............5
1.4 研究架構 ...............7
1.5 論文架構 ...............8
二、 影像辨識文獻探討 ...............9
2.1 影像辨識系統 ...............11
2.2 影像辨識系統性能評估方式 ...............12
2.3 特徵抽取 ...............15
2.3.1 離散傅立葉轉換 ...............16
2.3.2 離散餘弦轉換 ...............18
2.3.3 離散小波轉換 ...............20
2.3.4 熵與能量 ...............22
2.3.5 加伯濾波器 ...............23
2.3.6 奇異值分解 ...............25
2.3.7 卡亨南-拉維轉換 ...............28
2.4 計算智慧 ...............30
2.4.1 支持向量機 ...............31
2.4.2 k個臨近法............... 33
2.4.3 貝氏分類器 ...............34
2.4.4 調適性類神經模糊推論系統 ...............36
2.4.5 粒子族群最佳化 ...............38
三、 本論文提出之影像紋理辨識方法...............40
3.1 現存影像紋理特徵結合常用分類器之探討............... 40
3.2 SWSIR方法之設計 ...............42
3.3 智慧型影像紋理辨識方法 ...............45
3.4 智慧型彩色影像紋理辨識方法 ...............46
四、 實驗結果 ...............48
4.1 影像資料集設計 ...............48
4.1.1 USC資料集............... 49
4.1.2 Brodatz資料集 ...............51
4.1.3 VisTex資料集 ...............54
4.1.4 Sun-Harvest資料集 ...............55
4.1.5 Coffee beans資料集............... 57
4.2 分類器參數設定 ...............60
4.3 效能評估 ...............63
4.3.1 現存影像紋理特徵結合常用分類器效能評估...............63
4.3.2 SWSIR方法效能評估 ...............65
4.3.3 CSWSIR效能評估 ...............66
4.3.4 CSWSCIR效能評估 ...............67
4.3.5 CSWSIR、CSWSCIR與現存方法比較...............68
五、 結論及未來研究...............70
參考文獻 ...............72
附錄一…............... 78
附錄二… ...............90
Extended Abstract
簡歷

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