在本篇論文中，我們提出了應用在擴增實境的即時視覺追蹤技術，其視覺感測元件為一般的USB攝影機，用以捕捉來源影像；而被追蹤的物體為含有特定圖案且其尺寸已知的自製標籤。我們所提出的方法使電腦能感知各個標籤是否存在於攝影機的視野之中、該標籤具有何種身分，以及得知該標籤的姿態與位置。有了這些資訊，電腦即可將各個數位三維物件合成於指定的標籤上，如此，使用者就可以在與標籤互動的同時，直接與虛擬物件互動。
　　在本論文中所描述的追蹤方法包含四邊形偵測、標籤辨識，以及三維的姿態與位置估算，其中四邊形偵測的方法係植基於邊緣檢測，亦即在來源影像中，我們利用邊緣及其骨架與方向來搜尋四邊形的頂點，與植基於二值化的方法相比，我們的方法所適用的環境明暗變化範圍較大，接著，我們利用所搜尋出的頂點，將該四邊形正規化，以進行標籤辨識，並利用這些頂點來估算在真實世界中，標籤與攝影機之間的空間關係；為了將辨識錯誤降到最低，我們標籤辨識方法中的正規化處理程序，除了將四邊形的形狀正規化之外，也對其顏色進行正規化，於此，我們所設計的每張標籤都含有一個16位元的編碼，這提供了一個可觀數量的標籤供使用者應用。另外，在已知攝影機的垂直視角與焦距長度的情況下，應用我們的姿態與位置估算方法之前，不需要進行攝影機校正。
　　根據實驗的結果，在使用一個時脈頻率為2.4 GHz的單核心處理器，且同時追蹤5張標籤時，一張320 × 240影像的平均處理時間為9.4毫秒，相當於每秒可處理104.6張影像；而在追蹤相同數量的標籤時，一張640 × 480影像的平均處理時間為32.7毫秒，相當於每秒可處理30.6張影像，這顯示我們提出的方法能夠同時對於多個物件進行即時追蹤。

In this thesis, we present real-time visual tracking techniques applied for augmented reality. The visual sensor employed is a common USB camera for capturing source images, and the objects tracked are the artificial square markers with particular patterns and given sizes. Our proposed method enables computers to sense each marker whether exists in the sight of the camera, which identity the marker has, and what pose and position it is. With this information, computers can superimpose virtual objects on the specific markers respectively, so that users can interact with the virtual objects simultaneously by interacting with the markers.
The tracking method described in this thesis includes the quadrangle detection, marker recognition, and three-dimensional pose and position estimation. Our quadrangle detection method is based on the edge detection, and we use the edges with their skeletons and directions to search the vertices of quadrangles in the source image. In comparison with a threshold based method, our method is able to work in a wider range of the environmental lightness. The vertices are used to normalize the corresponding quadrangle for marker recognition and to estimate the spatial relations between the marker and the camera in the real world. The normalization process in our marker recognition method is not only to normalize the shape of the quadrangle but also to normalize the color of it for minimizing the errors of the marker identification. Each of our designed markers contains a 16-bit code which gives a good variety of identities to them. Our pose and position estimation method is camera-calibration free as long as the vertical angle of view and the focal length of the camera are given.
According to the experimental results, the average processing time of a 320 × 240 image for tracking 5 markers is 9.4 milliseconds corresponding to 104.6 FPS, and it takes 32.7 milliseconds corresponding to 30.6 FPS on an average to process a 640 × 480 image for tracking the same number of markers with a 2.4 GHz, single-core processor. It means that our tracking techniques can track multiple objects in real time.

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