Image matching via the local neighborhood for low inlier ratio
Weiqing Wang, Yongrong Sun, Zhong Liu, Zhantian Qin, Can Wang, Jinchang Qin · Journal of Electronic Imaging · 2022
Establishing reliable correspondences in an image pair is a prerequisite and crucial in computer vision. It remains a difficult topic to separate true and false matches in the given putative dataset with a low inlier ratio. To address this problem, an image matching method is proposed, via the local neighborhood of feature points. Grid-based motion statistics are initially engaged to preprocess the putative dataset, especially in which the inlier ratio is low. The local neighborhood distributions of feature points are then collected as the quality function of correspondences. Progressive sample consensus is next employed to estimate a global deformation for removing false matches. Robust experiments on nine typical image pairs with low inlier ratios demonstrate the superiority of our proposed method over five state-of-the-art methods. The comparison experiments on the Oxford dataset illustrate that our method outperforms the other five image matching methods.