基于归一化边缘互信息与自适应加速粒子群的图像配准方法  

Adaptive accelerated particle swarm algorithm for image registration based on normalized edge mutual information

作　　者：冯雪芳[1] 吴锡生[1] 

机构地区：[1]江南大学物联网工程学院,江苏无锡214122

出　　处：《计算机工程与科学》2013年第1期119-123,共5页Computer Engineering & Science

基　　金：国家自然科学基金资助项目(60973094)

摘　　要：传统的归一化互信息配准方法未利用图像的空间信息,当图像中混有一定噪声时,会出现误配准。边缘是图像最基本的特征之一,为了改进归一化互信息方法,提高图像配准的精度,加快收敛速度,将图像的边缘信息与灰度信息自适应地结合,形成归一化边缘互信息测度(NCMI),提出一种基于加速因子的自适应加速粒子群优化算法(AAPSO)来优化基于NCMI测度的图像配准。AAPSO算法通过对解排序,将指定数量的劣解进行进化加速来引导粒子的飞行,并对自适应惯性权重公式加以改进,提高了算法的收敛性,防止早熟收敛并增加优化解的多样性,同时加入加速因子来提高收敛速度。实验结果表明,该方法配准精度高,速度快,具有较强的实用性。The traditional normalized mutual information neglects the spatial information, so the reg- istration result will be incorrect when the image mixes with noises. Edge is the basic character of image, in order to solve the drawback of the normalized mutual information method, improve precision and speed up the convergence, we combines the image edge information with the gray information adaptively to form the normalized edge mutual information measure （NCMI） and propose an adaptive accelerate particle swarm optimization algorithm （AAPSO） based on the accelerated factor. The AAPSO is used for image registration based on the NCMI. By sorting the solutions, a specified number of worst solu- tions will be forced to accelerate in order to determine the direction of global solution, and we also im- prove the adaptive inertia weight formula, thereby it improves convergence, prevents premature conver- gence and increases the diversity of the optimal solution. Meanwhile, the AAPSO algorithm adds the ac- celerated factor to improve convergence speed. Result shows that the method has a high registration pre- cision, fast registration speed, and it has strong applicability.

关 键 词：图像配准 归一化互信息 归一化边缘互信息 粒子群优化 AAPSO算法 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]