基于核函数的活动轮廓模型  

Active Contours Driven by Kernel-Based Fitting Energy

作　　者：朱晓舒[1,2] 孙权森[1] 夏德深[1] 孙怀江[1] 

机构地区：[1]南京理工大学计算机科学与工程学院,南京210094 [2]南京师范大学分析测试中心,南京210046

出　　处：《计算机辅助设计与图形学学报》2015年第3期388-393,共6页Journal of Computer-Aided Design & Computer Graphics

基　　金：国家自然科学基金(60773172)

摘　　要：为了改善活动轮廓模型的分割精度和效率,提出一种基于核函数的活动轮廓模型.该模型采用鲁棒的非欧氏距离度量构造能量泛函,提高了模型的分割精度;使用指数类型的核特征函数来提升收敛速度;最后在模型中还加入了水平集正则项,以避免水平集的重新初始化.实验结果表明,文中模型在分割精度和分割效率上都要强于Chan-Vese模型.In this paper, a new region-based active contour model using kernel-based fitting energy is proposed to improve the accuracy and efficiency of segmentation. The proposed kernel-based fitting energy is defined as a kernel function inducing a robust non-Euclidean distance measurement to segment images more effectively. In addition, an exponential-type kernel-based function in our model is used, which leads to faster converge. At last, to avoid costly computation of re-initialization widely adopted in traditional level set methods, we introduce a new penalty energy as a regularization term. Experimental results demonstrate that our model can segment images more precisely and much faster than the well-known Chan-Vese model.

关 键 词：图像分割 CHAN-VESE模型 水平集方法 核特征函数 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]