基于极线几何的统计优化特征匹配算法

Statistical optimization feature matching algorithm based on epipolar geometry

针对基于特征点的图像匹配中匹配数目不多以及重复结构下匹配较差等问题,提出了一种基于极线几何的统计优化特征匹配算法。利用正确匹配特征点之间满足对极约束的特点,从而可以减小特征点搜索区域,避免由于重复结构引起的误匹配对。首先使用一个小的特征点样本估计图像之间的基本矩阵,并利用它结合对极约束模型来引导特征匹配;然后利用基于特征点主方向和尺度信息的统计优化方法进一步消除误匹配,得到最终匹配结果。实验结果表明,该算法对图像的旋转和缩放变换具有良好的鲁棒性,匹配精度和数目有了很大提升,对于具有重复结构的图像匹配效果也较好。

To solve the problems of small matching number and poor matching with repetitive structures in image matching based on feature points, a statistical optimization feature matching algorithm is proposed based on the epipolar geometry. As correct matching feature points satisfy the epipolar constraint, the feature points search region can be reduced and the mismatches caused by repetitive structures can be avoided. Our approach first uses a small sample of features to estimate the fundamental matrix between images and leverages it for guiding feature matching based the epipolar constraint model. Then we use the statistical optimization method based on scale and main direction information of feature points to further eliminate mismatches and get the final matching results. Experimental results show that our algorithm also has good robust- ness against image rotation and scale transformation, and the matching precision and number have been greatly improved. Our algorithm is also effective for image matching with repetitive structures.