基于特征的自适应正则化配准算法  

Adaptive regularization registration algorithm based on feature

作　　者：马金光[1] 王振松[1] 刘晓云[1] 

机构地区：[1]电子科技大学自动化工程学院,成都611731

出　　处：《计算机应用研究》2011年第6期2358-2360,共3页Application Research of Computers

基　　金：国家自然科学基金资助项目(40801171)

摘　　要：AndriyMyronenko提出了一种自适应正则化的方法并将其应用于非刚性图像的配准,该方法在配准速度和配准精确度方面都取得了比较好的效果;但该方法对变形场初始值比较敏感,选择不当则会陷入局部极小值而不能得到理想的配准结果。为了使原始算法得到更广泛的应用,引入了基于特征点的粗配准方法,得到了与真实变形场更加接近的初始变形场,从而摆脱了局部极小值的困扰,得到了正确的配准结果。实验证明,改进后的算法在应用范围和配准精度上都有了提高。Andriy Myronenko proposed an adaptive regularization method and applied it to non-rigid image registration.This method could achieve a good effect at registration speed and accuracy.But it made a failure registration because of trapping into local minima when chose the initial deformable filed irrelevantly.To using the algorithm extensively,this paper gained the initial value of the deformable field by rough-registration based on feature point.Because the initial deformable field was close to the real value,it could overcome the local minima and format a correct result.By this way,it enhances the practicality and the accuracy of the algorithm.

关 键 词：自适应正则化 局部极小值 粗配准 非刚性配准 特征 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]