摘要
通过对匹配模型中邻接矩阵的均衡化分析,在概率框架下提出一种新的特征匹配算法。采用重启动的随机游走方法建立并求解概率模型,并对匹配邻接矩阵进行了均衡化分析,提出了一种有效的双向均衡方法。方法不仅考虑了两个待匹配特征点的全部几何关联以及各项关联之间的权重值,而且考虑了关联权重的均衡性,从而可加强匹配的区分度,提高匹配的准确性。经实验证明,所提出的算法对几何畸变干扰和异常值都具有很好的鲁棒性,且适用于多种点匹配场合,在目标定位和目标识别中具有较强的适应性,有较好的实用价值。
Anew algorithm of feature matching is proposed after balancing analysis of adjacency matrix of the matching model in a probabilistic framework. A probabilistic model is established and solved using Random Walks with Restart （RWR）. Then a balancing analysis to the adjacency matrix of RWR is taken, and an efficient method for bidirectional balance is presented. The approach considers not only all the interaction of the two candidate feature point sets and the weight of each relevance, but also the balancing of all relevance weight. It improves the discriminative and accuracy performance of matching. The experimental results confirm that the method is robust to outliers and geometric deformation, accurate in terms of matching rate in various matching applications, and robust and practicable in the object location and the object recognition
出处
《光电工程》
CAS
CSCD
北大核心
2011年第2期78-83,共6页
Opto-Electronic Engineering
基金
中国博士后科学基金(20080430161)
中央高校基本科研业务费专项资金(JUSRP10926)资助项目
