A Geometrical-Information-Assisted Approach for Local Feature Matching

This paper presents a geometrical-information-assisted approach for matching local features. With the aid of Bayes’ theorem, it is found that the posterior confidence of matched features can be improved by introducing global geometrical information given by distances between feature points. Based on this result, we work out an approach to obtain the geometrical information and apply it to assist matching features. The pivotal techniques in this paper include exploiting elliptic parameters of feature descriptors to estimate transformations that map feature points in images to points in an assumed plane; projecting feature points to the assumed plane and finding a reliable referential point in it; computing differences of the distances between the projected points and the referential point. Our new approach employs these differences to assist matching features, reaching better performance than the nearest neighbor-based approach in precision versus the number of matched features.