基于同模型匹配点聚集的图像多匹配模型估计算法

Image multi-matching model estimation algorithm based onaggregation of matching points of same model

宽基线或大视角图像间多匹配模型的估计是图像处理中一项非常有挑战性的任务。现有算法虽然能较好估计图像间的多匹配模型及其内点集,但是其结果容易出现匹配对错误分配的问题。为了精确估计图像间的多匹配模型从而分配匹配对,提出一种基于同模型匹配点聚集的图像多匹配模型估计算法(AMPSM)。首先,为提升正确匹配对占比,根据近邻区域内正确匹配对的分布特点对错误匹配对进行过滤;然后,根据匹配点所属不同匹配模型程度查找疑似的多模型的交集点,即干扰点,同时,为了降低干扰点对匹配对分类精度的影响,将其去除;之后,为了提高同模型匹配点的聚集程度,根据抽样过程中同模型内点与其点集重心的距离动态移动位置;最后,通过基于高斯核的Mean Shift算法对聚集后的匹配点分类,进而得到多匹配模型。将所提算法分别与基于经典框架的算法RANSAC、PROSAC、MAGSAC++、GMS、AdaLAM、PEARL、MTC、Sequential RANSAC和基于深度学习的算法SuperGlue、OANet、CLNet、CONSAC等进行比较,结果表明该方法内点率可提高30%以上,多模型估计的错分率可降低8.39%以上,即所提方法在错误匹配对过滤和多模型估计等方面具有显著优势。

The estimation of multiple matching models between wide baseline or large angle images is a quite challenging task in image processing.The existing algorithms can be used to estimate multiple matching models and their inliers between images well,but their results are prone to matching pairs mis-classification issues.In order to accurately estimate the multiple matching models and allocate matching pairs,this paper proposed an image multi-matching model estimation algorithm based on the aggregation of matching points of the same model(AMPSM).Firstly,for improve the proportion of correct matching pairs,it filtered out incorrect matching pairs based on the distribution characteristics of correct matching points in the neighboring region.Furthermore,based on the different matching model degrees to which the matching pairs belong,searched for the suspected intersection matching pairs of multiple models,that was interference matching pairs.Meantime,for reducing the impact of interference matching pairs on the accuracy of matching classification,they were removed.Afterwards,for improve the clustering degree of matching points with the co-model,the position was dynamically moved based on the distance between the points within the same model and the center of gravity of the point set during the sampling process.Finally,classifying clustered matching points by Mean Shift to obtain a multi matching model.And the proposed method was compared with classical framework based algorithms RANSAC,PROSAC,MAGSAC++,GMS,AdaLAM,PEARL,MTC,Sequential RANSAC,and deep learning based algorithms SuperGlue,OANet,CLCNet,CONSAC,etc.Results indicate over 30%increase in the inlier rate,8.39%reduction in the mis-classification rate of multi model estimation.It is concluded that the new algorithm has significant advantages in incorrect matches filtering and multi-model estimation.