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研究生:陳佩伶
研究生(外文):Pei-Ling Chen
論文名稱:紋理影像分類基於小波包粗集和共生矩陣粗集方法
論文名稱(外文):Classifying Texture Images Based On WP-RS and COM-RS Methods
指導教授:鄭景俗鄭景俗引用關係
學位類別:碩士
校院名稱:國立雲林科技大學
系所名稱:資訊管理系碩士班
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:英文
論文頁數:48
中文關鍵詞:粗集共生矩陣紋理分類小波包轉換
外文關鍵詞:Texture classificationWavelet packet transformRough setsCo-occurrence matrix
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近年來,紋理影像是一個非常熱門的議題。多解析度分析的方法分析影像,優於其他傳統統計的方法,例如:小波轉換和小波包轉換。應用小波包分析在紋理分類上,可以獲得不錯的分類效果。在本篇論文中,提出兩個新的混合方法,用在不變的紋理影像區塊:小波包粗集和共生矩陣粗集系統的方法。換句話說,特徵萃取和分類的過程分別使用小波包粗集和共生矩陣粗集的方法。本研究使用兩個影像資料庫來進行驗證:Brodatz 黑白影像資料庫和彩色建築材質影像。分別從Brodatz資料庫和建築材質影像,選取二十張512×512的影像,再從每一類別隨機選取60張32×32的影像區塊,這些影像區塊可能重疊或沒有重疊,再分別使用在小波包粗集和共生矩陣粗集的方法作訓練和測試,挑選每一類別的30張32×32影像區塊做訓練,剩餘的影像區塊做測試。本研究的實驗次數超過600,000回合以上,實驗結果顯示本篇論文提出的共生矩陣粗集的方法優於其他方法。
In recent years, texture image analysis is a popular issue. Applying multi-resolution analysis methods to image analyzing such as wavelet and wavelet packet decompositions are more superior to other classic statistical methods. Therefore, the wavelet packet analysis has been intensively used for texture classification with encouraging results. In this paper, two new hybrid methods for invariant pixel regions texture image classification are proposed, which are named wavelet packet rough sets (WP-RS) and co-occurrence matrix rough sets (COM-RS), i.e. the feature extraction and classification processing are performed using wavelet packet decomposing and co-occurrence matrix, combined with rough sets theory in this study respectively. In this research, there are two datasets used to verify the performances of proposed method, i.e. Brodatz image database and Building Material image database. In experiment and verification, sixty texture image regions, of size 32×32, are randomly selected (overlapping or non-overlapping) from each of these twenty images of size 512×512, which obtained from Brodatz image database and Building Material image database, respectively. Thirty texture image regions, of size 32×32, and other thirty image regions from the same image are used for training and testing in the WP-RS and COM-RS methods, respectively. The experiments have performed more than 600,000 rounds. The experimental results show that the proposed COM-RS method outperforms other methods.
摘要 i
Abstract ii
致謝 iii
Contents iv
List of Tables v
List of Figures vi
1. Introduction 1
1.1 Research background 1
1.2 Research motivations and research objective 2
1.3 Research limitations 3
1.4 Organization of this thesis 4
2. Related works 5
2.1 Previous related researches 5
2.2 Discrete wavelet packets transform 7
2.3 Co-occurrence-matrix 11
2.4 Rough sets theory 14
3. Methodology 18
3.1 Research Framework 18
3.2 Proposed Algorithm 19
4. Verifications and Comparisons 25
4.1 Brodatz image database 25
4.1.1 WP-RS method (Brodatz image database) 26
4.1.2 COM-RS method (Brodatz image database) 27
4.2 Building Material image database 29
4.2.1 WP-RS method (Building Material image database) 29
4.2.2 COM-RS method (Building Material image database) 30
4.3 Comparison 31
4.4 Findings and discussions 33
5. Conclusion 35
Reference 36
Appendix A. The all result of WP-RS (Brodatz image database) 39
Appendix B. The all result of WP-RS (Building Material image database) 40
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