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研究生:陳世章
研究生(外文):Shih-Chang Chen
論文名稱:影像輪廓偵測
論文名稱(外文):Detection of Image Contours
指導教授:辛正和
指導教授(外文):Chengoh Hsin
學位類別:碩士
校院名稱:逢甲大學
系所名稱:通訊工程所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
畢業學年度:92
語文別:中文
論文頁數:103
中文關鍵詞:輪廓偵測邊緣偵測影像分割
外文關鍵詞:Edge detectionContour detectiontexture-like
相關次數:
  • 被引用被引用:11
  • 點閱點閱:953
  • 評分評分:
  • 下載下載:127
  • 收藏至我的研究室書目清單書目收藏:1
本論文的主要目的是發展一個影像邊界輪廓偵測方法。早期的邊緣偵測無法有效的從紋理區域分離出物件的輪廓,此結果將造成影像分割、影像重建及其他後續影像處理上的困難。為解決上述問題,本論文研究的課題為建立輪廓模型和正規化抑制紋理理論,提出三種機制:能量模組、微分模組和曲率模組去建立輪廓模型,再配合本論文提出的正規化方式達到有效的紋理抑制及保留應有的輪廓特徵。

The purpose of this thesis is to develop a unified image contour detection scheme. Traditional edge detection methods are unable to distinguish between contours of objects and edges originating from textured regions. It results in difficulty in subsequent processes. The proposed scheme will resolve this difficulty.
The research topics include developing of contour extraction model and suppression of texture-like edges. The contour extraction model consists of three modules︰energy module, differentiation module and curvature module. By incorporating normalization procedure into three modules, image contours are extracted by suppressing texture-like edges.


摘 要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 viii
表目錄 xi
第一章 緒論 1
1.1 簡介 1
1.2 研究動機 2
1.3 研究目標 3
1.4 內容大綱 4
第二章 文獻回顧 5
2.1 非傳統架構的技術 5
2.2 改善傳統架構的技術 7
2.3 生物生理學的技術 9
第三章 區域灰度值調整 12
3.1 數學模式 12
3.2 能量計算 14
3.3 積次微分和偶次微分 15
3.3.1 特徵的結構性問題 15
3.3.2 特徵的方向性問題 17
3.4 匹配權重值和能量權重值 18
3.5 振幅和邊緣標度的關係 21
3.6 轉換對紋理響應的影響 23
3.7 搜尋法 24
3.8 特徵擷取比較 27
3.8.1 連續性 27
3.8.2 交點和角點 27
第四章 邊界輪廓偵測模型 31
4.1 特徵擷取 31
4.1.1 等方向高斯快速法 31
4.1.2 多重解析度的冗餘計算 33
4.2 輪廓模型 35
4.2.1 能量模組 35
4.2.2 微分模組 40
4.2.3曲率模組 42
4.3 理想輪廓模型 44
4.3.1 邊模型 45
4.3.2 線模型 47
4.3.3 交點角點模型 49
4.4 紋理抑制 51
4.4.1 理論的抑制機制 54
4.4.2 理論機制和自然影像的配合 59
4.4.3 正規化結果 62
4.4.4 取門檻值方式和結果 65
4.5 輪廓獲得 69
4. 6 細線化 71
第五章 實驗結果和比較 75
5.1 對特徵擷取後續的幫助 75
5.2 與近年來較新技術比較 77
5.3純紋理測試 80
5.4 人工影像測試 82
第六章 結論 87
參考文獻 88
作者簡介 91


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