为了更好地利用Snake模型来提取彩色图象中的物体轮廓，因而对Snake原型提出两点主要改进，即针对snake模型的手工初值设置问题，通过引入彩色聚类预处理过程来减少对人的依赖，首先，采用色彩聚类算法对原始图象进行分割，然后用改进的边缘追踪算法提取有意义区域的边缘，并用这一结果作为Snake模型的初值；然后针对Snake原型应用于彩色图象时出现的失真问题，通过对出错原因的分析，重新设计了Snake的外部能量函数，同时用像素在加权HSI颜色空间中的欧氏距离代替传统方法中常用的像素灰度的差分来近似图象梯度；最后，进行了对比实验，实验结果证明，改进后的算法，特别是在处理彩色图象时，大大优于原始方法．

In order to improve the effectiveness of Snake active model, the deformable template approach of contour-tracking algorithm, in color images, this paper mainly focuses on resolving two problems. First, to reduce the difficulty of handcrafting initial contour for Snake, the authors introduce the pretreatment procedure. They first use color-based clustering algorithm to divide an image into several regions, then use improved graph-searching algorithm to get the contour of independent regions which may be important for further recognition, and take this result as the initial contour. Second, to avoid the distortion contour produced by Snake model when applying it to tracking the contour in some color images, they redesign the external energy function of Snake. They use neighborhood pixels' Euclidean distance of the colors in weighted HSI color space, instead of grey level difference in traditional algorithms, to approximate the grads of image, and take the square of the distance as external energy function to keep the active contour staying at the place where the real contour locates. At last, comparing prototype of Snake model with the new method, authors illustrate that the improvement is considerable, especially when color images are involved.