Feature matching method study for uncorrected fish-eye lens image

Abstract

Because of the further from the center of image the lower resolution and the severe non-linear distortion are the characteristics of uncorrected fish-eye lens image, the traditional feature matching method can’t achieve good performance in the applications of fish-eye lens, which correct distortion firstly and then matches the features in image. Center-symmetric Local Binary Pattern (CS-LBP) is a kind of descriptor based on grayscale information from neighborhood, which has high ability of grayscale invariance and rotation invariance. In this paper, CS-LBP will be combined with Scale Invariant Feature Transform (SIFT) to solve the problem of feature point matching on uncorrected fish-eye image. We first extract the interest points in the pair of fish-eye images by SIFT, and then describe the corresponding regions of the interest points through CS-LBP. Finally the similarity of the regions will be evaluated using the chi-square distance to get the only pair of points. For the specified interest point, the corresponding point in another image can be found out. The experimental results show that the proposed method achieves a satisfying matching performance in uncorrected fish-eye lens image. The study of this article will be useful to enhance the applications of fish-eye lens in the field of 3D reconstruction and panorama restoration.