Abstract
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 (1) exploiting elliptic parameters of feature descriptors to estimate transformations that map feature points in images to points in an assumed plane; (2) projecting feature points to the assumed plane and finding a reliable referential point in it; (3) 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.
Highlights
For matching local features, the threshold-based method and the nearest neighbor-based approach (NNA) are two fundamental strategies
Since positions of matched features can be seen as samples generated from two images related by a certain homography, RANSAC [8] is usually applied to exclude the impact of outliers on the estimation [9,10,11,12,13,14,15,16], improving matching effect of local features
Motivated by approaches above built on local geometrical information, we discuss a new method assisted by geometrical information to improve the performance for matching features
Summary
The threshold-based method and the nearest neighbor-based approach (NNA) are two fundamental strategies. LLT [28] exploits local geometrical constraints to estimate the consensus set which comprises inliers in matches between two rigidly or nonrigidly transformed images. Besides those consensus-estimation-based methods, approaches without estimating consensus are studied. LPM [31] applies local neighborhood structures to determine true matches, and based on it, GLPM [32] introduces a set with more confidence of including true matches, which are obtained by the distance ratios of local descriptors, and reaches better performance than LPM Another local-geometricalinformation-based technique is proposed by [33] for describing features and matching features, which exploits topological relationship amid local features. In the case of matching some deep features, convolutional neural networks are employed [34]
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