Moment Symmetry: A novel method for interest point detection to match blurred and non-blurred images

To match a pair of images, currently lots of local features can be used. Many of them show good performances for image changes such as rotation, resizing, and occlusion etc. However, matching between a blurred (especially, motion-blurred) image and a non-blurred image is still a challenging task although it is required for many image/video applications. Local feature matching is usually composed of three steps: interest point detection, feature description for each interest point, and matching these features. Unfortunately interest point detection is not robust for strong blur in most existing methods. In this paper, we present Moment Symmetry (MS) to solve this problem. MS is a new concept of symmetry against traditional symmetry. By using MS, we can extract the same interest points from a blurred image and a non-blurred image in a simple way. Experimental results show MS outperforms existing methods as a novel interest point detector.