此篇論文揭露了兩種適用於二維影像轉三維影像過程中所需的影像深度資訊之半自動萃取方法。因為近來對於三維立體影像的需求，又三維影像內容的資源不是那麼普遍。如果我們也想方便的享受到逼真的立體視覺效果，勢必需要開發建立於低成本和高效能的後端轉換方法，把欲觀看的二維影像快速的轉換為三維立體影像。對於靜態背景的二維輸入影像，我們提出一個利用前景切割和單眼視覺深度線索中的消失線技術。根據前景切割演算法分離出來的背景與前景，使用者可以根據後天學習到的視覺經驗在初始化過程給予電腦判斷背景影像中距離觀賞者遠近的訊息，之後前景再根據計算出的背景深度獲得適當的自身深度值。從實現結果比較中也顯示，在立體視覺觀感跟其他參考資料差不多的情況下，此演算法在CIF規格的影像中可以達到0.17s/frame的處理速度。此外，我們沿用上述的概念，提出另一個適用於動態背景的二維輸入影像轉換的方法。利用估測運動向量和運動補償機制，計算出所謂背景的相對移動速度，進而用來分離出所謂的背景與前景，取代了前景切割的步驟。根據實驗結果顯示，雖然此演算法在動態背景的轉換上尚不能達到相對於靜態背景的轉換品質，但是此演算法有較廣的使用層面，並且在CIF規格的影像中更可以達到0.15s/frame的處理速度。There are two semi-automatic depth map extraction methods for stereo video conversion presented in this thesis. Due to demand of 3D visualization and lack of 3D video content, we must develop low cost and high efficiency post processing methods to convert efficiently from 2D to 3D video if everyone wants to enjoy vivid 3D video.For static background video sequence, we proposed a method that is combined foreground segmentation with vanishing line technology of monocular depth cue. According to the results of separated foreground and background from foreground segmentation algorithm, viewer could use their acquired visual experience to assign computer some depth information of background at initialization step. Then, foreground would be obtained relative depth information form background depth map. This algorithm could be operated at 0.17s/frame in CIF size video under nearly 3D visualization to other references from our experimental results.Moreover, we proposed another conversion method followed conception as mentioned above for dynamic background video sequence. Foreground segmentation was replaced by relative velocity estimation based on motion estimation and motion compensation. Although this method is not able to attend equally quality of foreground segmentation method, this method still has wide utility and could be operated at 0.15s/frame in CIF size video.
