中文摘要：

本文对活动轮廓模型的外部能量项进行改进,针对灰度图像分割提出了一种新的自适应图像分割模型,并将它推广,建立了矢量图像分割模型.新模型耦合了快速边缘积分方法和简化统计方法,充分考虑到图像区域和边缘的先验信息,可根据不同的条件概率密度函数构造不同图像分割模型.文中还基于高斯型概率密度函数建立分割模型实例,结合应用高效且无条件稳定的AOS算法分别对灰度图像和矢量图像（RGB）进行分割实验,并将本文提出的方法与经典的快速边缘积分方法进行比较,结果表明本文的分割方法准确性较高,且具有良好的抗噪性,是行之有效的.

英文摘要：

In this paper, we improved the external energy of active contour model, proposed a new adaptive segmentation image model aimed at gray-level image and extended it to the vector value image segmentation. New model couples the fast edge integration and simply statistical methods. The prior information of regions and boundaries of image has been considered sufficiently in this model, and different segmentation models can be constructed based on different probability density function respectively. An instance based Ganssian probability density function has been given in this paper and the AOS scheme that is efficient and unconditional stable has been used to segment the gray image and the vector values image. Compared with Fast Edge Integration method, the experiment results show that the new approach is more accurate and robust and can obtain very good partition.