图像特征点集匹配的稳健非线性投影NMF方法

Feature Point-set Matching of Images Using Robust Nonlinear Projective Nonnegative Matrix Factorization

包含相同目标的图像由于可能存在结构差异而导致特征匹配困难、不精确,针对该问题提出了一种新的匹配方法。首先,提出一种稳健的非线性投影非负矩阵分解方法(RNPNMF),利用RNPNMF得到特征点集的共同投影空间;然后,计算特征点集在共同投影空间的投影,利用特征点集在共同投影空间上的投影实现点集的精确匹配。最后,为验证本文方法的有效性,分别对光学图像和SAR图像进行了实验,实验结果表明:和现有方法相比,本文所提方法能更精确有效的实现特征点集的匹配,同时,应用于图像配准也得到了很好的结果。

A novel matching method based on Robust Nonlinear Projective Nonnegative Matrix Factorization （RNPNMF） is proposed to find the correspondence among different images containing the same object.We show how the features point-sets can be matched using their common projection space.The contribution can be divided into two parts.Firstly,a robust RNPNMF method is developed to capture the common projection space of the feature point-sets.Secondly,a matching approach is derived from the projections on the common projection space of the feature point-sets.Finally,two experiments are conducted to verify the effectiveness of the proposed method.The experimental results show that compared with the existing method,our method is more effective in matching the feature point-sets and can be generalized well to registration.