基于最大似然估计准则的特征匹配点提纯算法  

Feature matching-point purification algorithm based on maximum likelihood

作　　者：史素霞 杨会君[1] 杨茜 张建锋[1] Shi Suxia;Yang Huijun;Yang Qian;Zhang Jianfeng(College of Information Engineering,Northwest A&F University,Yangling Shaanxi 712100,China)

机构地区：[1]西北农林科技大学信息工程学院

出　　处：《计算机应用研究》2019年第12期3885-3888,共4页Application Research of Computers

基　　金：陕西省重点研发计划资助项目(2018NY-127)

摘　　要：图像特征匹配的准确度直接影响着图像分析与处理的效率与性能,所以要对图像的特征匹配点进行提纯和过滤。首先使用SIFT算法从图像中提取显著特征,建立粗略的匹配关系,利用最近邻比策略初始化特征匹配点的匹配概率,然后基于混合模型的最大似然估计采用EM算法建立匹配点之间的空间转换模型。EM迭代收敛之后,通过其对应关系过滤掉错误的匹配点。实验数据表明,本方法提纯的平均精度可以达到96. 8%,平均召回率为81. 6%,平均时间消耗为3. 1 s。采用该方法提取到的正确匹配点数高于其他算法,同时对包括大视角差、光线变化和仿射变换等大多数变换具有鲁棒性。The matching accuracy of image-pair features directly affected the efficiency and performance of subsequent image analysis and processing,so it was necessary to purify and filter the feature matching-points of the image-pair. Firstly,using the SIFT( scale-invariant feature transform) algorithm to extract the salient features from the image and established a rough matching relationship. And applying the nearest neighbor ratio to initialize the matching probability. Then,using EM( expectationmaximization) to establish a spatial transformation model between matching-points based on the maximum likelihood of the hybrid model. After the EM iteration converged,utilizing the correspondences to filtered out the wrong matching-points. The experimental results demonstrate that the average accuracy of the proposed method can reach 96. 8%,the average recall rate is81. 6%,the mean time consumption is 3. 1 s. This method is higher than other algorithm on the extracting number of correct matching point-pairs and is robust for most cases including a large viewing angle,image rotation and affine transformation.

关 键 词：图像特征匹配 最大似然估计 EM算法 最近邻比 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]