Enhanced Rotational Feature Points Matching using Orientation Correction

Abstract

Several techniques have been developed for estimating orientation assignment to make feature points invariant to the rotation for the purpose of matching. However, imperfect estimation of the orientation assignment may lead to feature mismatching and low number of correctly matched points. Besides, several possible candidates with high correlation values for one feature in the reference image may lead to matching confusion. In this paper, we propose a post-processing matching technique to increase the number of correctly matched points and at the same time solve these two issues. The key idea is to correct the orientation of features based on the relative rotational degree between two images which is estimated based on the orientation difference between major correctly matched points after first round matching. Our analysis of the proposed method shows that the number of correctly matched points can be increased up to 50% of the detected points in the reference image. In addition, some mismatched points due to similar correlation value in first round matching can be corrected. Moreover, the proposed algorithm can be applied to different states-of-the-art orientation assignment techniques.

Keywords

• Computer Vision
• Features and Image Descriptors
• Video Surveillance