臺灣博碩士論文加值系統

隨著數位影像處理技術的快速進步與視訊產品的大量普及，結合了影像追蹤技術的電腦系統被廣泛應用於日常生活的各種領域中，例如監視系統、智慧型交通系統、停車場管理系統等。這些電腦視覺系統不僅可取代傳統大量仰賴人工視力的工作，更可避免因人為的疲累所帶來的疏失；而且在時效上，更具有即時回報突發狀況的能力，因此能大幅降低整體系統的時間成本。本論文研究方向以發展影像追蹤演算法為目的，利用K-means分群法將樣板影像資料分群，並導入模糊數的觀念產生影像追蹤過程中所需之特徵向量，最後結合均值移動演算法計算樣板影像目前的位置，達到追蹤的目標。

With the popularization of video products and the rapid progress in visual techniques in recent years, the computer systems with video object tracking technology have been applied in various fields in daily life, such as surveillance systems, intelligent traffic control systems, parking lot management systems, and so on. The computer visual systems not only replace manpower in a large number of traditional jobs but also can avoid the errors induced from human tiredness. Meanwhile, it has the ability to timely report back the accident, therefore the time costs of the entire system will be reduced substantially. The direction in this research is to develop a robust video object tracking approach. By the use of K-means clustering algorithm to automatically divide the video object image into several clusters and the principle of fuzzy theory to generate the feature vector, the mean shift algorithm is then applied to calculate the position of candidate template to achieve good performance in object tracking.

中文摘要 Ⅰ
英文摘要 Ⅱ
致謝 Ⅲ
目錄 Ⅳ
圖目錄 Ⅴ
表目錄 Ⅷ
一、緒論 1
1.1 研究背景與動機 1
1.2 論文架構 2
二、文獻回顧 3
三、研究方法 9
3.1 系統架構 9
3.2 RGB色彩模型正規化 11
3.3 K-MEANS自我建構分群演算法 12
3.4 樣板模型之建立 18
3.5 均值位移 21
3.6 找出新的運動物體中心點 23
四、實驗結果與討論 24
4.1 參數設定 25
4.2 實驗結果 25
範例一 25
範例二 30
範例三 36
範例四 40
範例五 43
範例六 46
五、結論及未來展望 50
參考文獻 52

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