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研究生:吳聲遠
研究生(外文):Sheng-Yuan Wu
論文名稱:以調適性視窗聚焦量測法建立深度地圖並達成3D形狀重建
論文名稱(外文):3D Shape Recovery Based on Depth Map Generation Using Focus Measures in Adaptive Sized Windows
指導教授:黃博惠黃博惠引用關係
學位類別:碩士
校院名稱:國立中興大學
系所名稱:資訊科學與工程學系
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:96
語文別:中文
論文頁數:32
中文關鍵詞:3D形狀重建聚焦量測法深度地圖調適性視窗演算法
外文關鍵詞:3D shape recoveryfocus measuredepth map
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傳統的2D影像僅能顯示一物件之表面形狀及其平面方向之運行情況,而3D影像則多了Z軸座標來描述該物件與觀察者之間的距離,使其所觀測之物件具有一定的立體感。3D形狀重建(Shape Recovery)即是一個能將一物件由2D影像轉換為3D影像描述的方法,因此如何獲得一張準確的深度地圖一直是3D形狀重建的目標,其中對待測影像選取適當的聚焦量測法來建立深度地圖是一個很重要的部分,在過去文獻中也提出了許多方法,其中Sum Modified Laplcian (SML) 是用固定視窗大小作聚焦程度的衡量,若視窗過大,則建立的深度地圖會過度平滑;反之,若視窗過小,則建立的深度地圖受雜訊影響甚大,於是本論文以SML為基礎,提出了一個調適性視窗演算法。經實驗證明此演算法能改善視窗大小的影響因而獲得準確的深度地圖。
目錄
摘要 I
Abstract II
目錄 III
表目錄 V
圖目錄 V
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機與目的 1
1.3 研究範圍與限制 2
1.4 論文架構 2
第二章 相關文獻 3
2.1 3D形狀重建簡介 3
2.2 3D形狀重建技術介紹 3
2.2.1 聚焦測深(Depth From Focus)(DFF) 3
2.2.2 聚焦成形(Shape From Focus)( SFF) 4
2.3 聚焦量測方法介紹 5
2.2.1. 灰階值變異數(Gray-Level Variance)(GLV) 6
2.2.2. 影像梯度能量(Energy of image Gradient)(EOG) 7
2.2.3. 拉普拉斯能量(Energy of Laplcian)(EOL) 7
2.2.4. Modified Laplcian(ML) 7
2.2.5. Sum Modified Laplcian(SML) 7
2.2.6. Tenebaum’s algorithm(Tenengrad) 8
2.2.7. Spatial Frequency(SF) 8
第三章 方法流程 10
3.1 方法動機 10
3.2 分離背景與感興趣區域 11
3.3 調適性視窗演算法 11
3.4 邊界與雜訊點處理 14
第四章 實驗結果與分析 16
4.1 實驗方向 16
4.2 選取最大視窗大小與修正邊界與雜訊點的實驗 19
4.3 測試演算法效能的實驗 24
4.4 「固定視窗」及「調適性視窗」的方法的效能比較 27
第五章 結論與未來方向 30
參考文獻 31
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