基于最大熵和互信息最大化的特征点配准算法

点配准问题在机器视觉、医学图像等领域，有着非常重要的应用基础．通过在最大化熵原理的基础上，将互信息相似性测度引入到点配准算法中，提出了一种新的快速、准确的健壮性的点配准算法．首先建立起表示两个特征点集之间匹配对应关系的联合概率分布匹配矩阵，通过最大化熵和互信息最大化，建立起一个包含匹配矩阵和空间变换参数的新的能量函数，通过确定性退火算法，可以获得最优的匹配矩阵和空间变换参数，从而解决点的对应性问题和出界点(outliers)确定．实验结果表明，算法具有较强的鲁棒性，具有较高的配准精度和较快的计算速度．

A New Robust Point Registration Algorithm by Maximization of Entropy and Mutual Information

Point registration is an important problem in many fields,such as machine vision,medical image registration,etc. A new robust point registration algorithm is proposed by maximization of entropy and mutual information,which is very accurate and rapid. A novel aspect of the algorithm is the introduction of mutual information into point registration algorithm,and another aspect is the joint probability matrix,which can represent the correspondences of two point-sets. Then a new energy function,including the joint probability matrix and spatial mapping matrix,has been derived. By minimizing the above energy through a deterministic annealing algorithm,The estimation of spatial mapping parameters and joint probability matrix can be obtained. The experimental results show the validation and robustness of the algorithm.