物件輪廓線的取得在影像處理中占極重要的角色，且輪廓線取得的準確性與方便性，也是決定一影像處理系統好壞的重要因素。所謂的動態輪廓線模型，係指由系統或使用者給定初始輪廓線，然後再經由一變形演算法將輪廓線形變（Deformation）至正確輪廓線的位置上，這種取得輪廓線的方式，不但方便而且準確度也很高。
本論文整理了各種動態輪廓線模型，並且以離散動態輪廓線模型（Discrete Dynamic Contour Model）為基礎，設計了一套新的動態輪廓線模型。將傳統動態輪廓線模型所不足之處，如準確性及效率等方面加以改良。在新的模型之中，我們加強了影像前處理的部份，以減少變形過程中的不確定性，並讓影像中的物件邊緣突顯出來；另外在變形的過程中，利用線段截取的觀念，加速變形的過程，之後再進入評估步驟，將不合理的輪廓線片段重新做一次前處理，變形的步驟，直至通過評估為止。

A contour extraction plays an important role in image segmentation. A convenient method to obtain accurate contours is one of the key features of robust image processing system. The Active Contour Model was proposed to segment an object correctly in an effective and efficient manner. In this model, an initial contour is given first and then deforms according to a set of defined forces to obtain the final result.
In this thesis, we study several different active contour models, and designed a new active contour model based on Discrete Dynamic Contour Model. We improve the traditional active contour model’s drawback in terms of accuracy and performance. In the new active contour model, we enhance the image preprocess to reduce the uncertainty in deformation step, and emphasize the object edge response. In the deformation step, we use the concept of segment extraction to accelerate the deformation perform. Then, in the evaluation step, we eliminate the unreasonable contour segments and repeat the image reprocess and deformation steps over and over again until the evaluation result is acceptable.

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