物體辨識是電腦視覺領域中重要的任務之一，且現有方法大多以物體的紋理特徵資訊進行特徵描述及辨識處理。然而，在許多實際應用中，需辨識的物體並沒有充足的紋理資訊來描述，進而提升了物體辨識問題的難度，此稱為無紋理物體辨識。也因此，如何解決無紋理辨識問題是影像物體辨識在實際應用中一項重要的課題。本論文以現有的Line2D演算法為基礎，提出一個無紋理物體的辨識方法，其利用邊緣模板匹配的方式去偵測與辨識多種不同的無紋理物體。首先，對感興趣物體建立一參考模板影像，並針對此模板影像透過旋轉以及縮放的仿射轉換(affine transform)運算，產生各種姿態的參考模板影像，並建立此感興趣物體的模板影像資料庫。在偵測與辨識物體時，可利用建立好的模板影像資料庫，透過模板匹配方式對輸入影像進行匹配的動作，最後找出感興趣物體在輸入影像中的位置與角度姿態。實驗結果顯示，本論文提出之無紋理辨識方法不但可以有效辨識出不同種類的無紋理物體於輸入影像中的位置與角度資訊，並且能夠利用相似度資訊辨識物體之上下層關係。另外，所提出之辨識系統亦可達到即時處理的效能，在處理640x480影像解析度的條件下可達到每秒約23張以上的處理速度。此系統未來能夠與機械手臂作結合，套用於箱內隨機堆疊物夾取之應用上。 Object recognition is one of the important tasks in the field of computer vision, and most of the conventional methods use texture information of objects to produce feature descriptors for object recognition process. However, in many practical applications, the object to be recognized may not have enough texture information for extracting feature descriptors, greatly increasing the difficulty of the object recognition task. This problem generally is referred to as textureless object recognition. Therefore, how to solve the problem of textureless object recognition is an important issue in practical applications. In this thesis, a textureless object recognition method is proposed based on the existing Line2D algorithm. The proposed method employs an edge-based template matching method to detect and identify a wide variety of textureless objects. Given a reference template image of an object-of-interest (OOI), a template image database containing various postures of the OOI was firstly created by applying affine transformation with different rotating and scaling settings to the reference template image. Next, the edge-based template matching process is performed to detect and recognize the OOI by searching matches between the template image database and the input image. Finally, the position and angle posture of the OOI can be determined by the best match having the highest similarity measure. Experimental results show that the proposed method not only can efficiently recognize the type, position, and angle information of various textureless objects in the image, but also can identify up-down relationship between the recognized objects. In addition, the proposed method achieves real-time performance at least 23 frames per second (fps) in processing 640x480 images. In future work, the proposed object recognition algorithm will be integrated into a robot manipulator system to accomplish random bin-picking function for manipulating textureless objects.