结合先验形状和Mumford-Shah模型的活动轮廓分割

基于先验形状和Mumford-Shah模型的活动轮廓分割是一种抗噪声干扰、稳定的图像分割方法。该模型采用水平集方法，并结合活动轮廓模型、先验形状和Mumford-Shah模型来控制曲线演化。特定目标的先验知识可以有效地指导目标准确分割，经过主成分分析（PCA）法可以得到感兴趣对象形状的主要信息。通过对不同图片分割实验表明，针对特定的形状，该方法对杂乱背景、部分遮挡、缺失和强噪声的图片依然能得到满意的结果。

Combining prior shape and Mumford-Shah model for active contour segmentation

The active contour segmentation based on prior shape and M-S model has been widely used in image segmentation because of their computational efficiency and stability.Based on level set method,the novel model using the active contour model,prior shape and Mumford-Shah model can control evolvement of curve.Prior knowledge on certain object is a useful guidance in image segmentation.It exploits the prior knowledge of desired object’s shape through a Principle Component Analysis（PCA） method.By numerical experiments presented in the paper,the proposed approach is robust to cluttered background,partial occlusions,missing and noise with the specific shape.