電腦視覺系統已廣泛被應用到日常生活中，電腦立體視覺系統一般都利用兩個攝影機來同時取得同場景之兩張平面照片，再由此兩張平面照片來找出系統周遭之場景內各個物件間之遠近與關係位置。本研究是在探討如何由此兩張平面照片中各物件影像之相對應關係來求得實際場景上各個物件與攝影機間之距離。這論文中提出基於區域的相互關係去找到物體的深度訊息。 首先，針對一張未經過校正的立體影像進行影像分割動作，先分割出該影像中之各個物件的大略位置與大小。 然後將此資訊拿來與另一張未經處理之立體影像重疊，如此由已被切割之物件範圍即可當成兩張立體影像之相對應物件間之匹配基礎。因為，對應於已切割的物件在另一張未切割之影像上呈現的位置應就在此已切割物件所在的附近，此在有限範圍內之搜尋方式是採用基于區域性之匹配法。透過這種方法，在立體視覺的兩幅圖像之間的所有相對應區塊都可以被匹配，並且能夠有效地降低運算時間和增加匹配的品質。 因此，立體視覺的深度資訊可以被計算而獲得。經由實際實驗量，採用本法所求得之深度資料其誤差不會超過10%。

Computer vision system has been widely used in our daily life. In general, two cameras are used to capture the stereo images of the scene around the computer vision system. These two images are 2-D only. It cannot directly provide the depth information for the objects located in the scene around the system. However, the relative position among the objects in each of the image is the same, due to they are captured for the same scene. In this thesis, an area based correlation method is proposed to find the depth information of the two stereo images. In strictly speaking, the propose method can be classified as a hybrid one. Firstly, one of the stereo images is segmented to get the rough area which covered the object. After the segmentation and find out the locations of the objects in one image, it is then overlapped onto the other unprocessed image to locate the starting searching region. Due to the two images are obtained from the same scene around the system and the two cameras are fixed with a relative position, the objects appeared on the two images must have a fixed relative position. So the matched area located on the unprocessed image corresponding to the segmented object in the processed image can easily be found by local area correlation matching method. By using this method, all of the corresponding objects between two images of a stereo vision can be matched, and this approach can effectively reduces the computational time and improving the matching quality. Accordingly, the depth of the stereo vision can be calculated. From the experimental results, it is shown that the error rate of the calculated depth information for the proposed approach is fewer than 10%.

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