Central catadioptric image processing with geodesic metric

Because of the distortions produced by the insertion of a mirror, catadioptric images cannot be processed similarly to classical perspective images. Now, although the equivalence between such images and spherical images is well known, the use of spherical harmonic analysis often leads to image processing methods which are more difficult to implement. In this paper, we propose to define catadioptric image processing from the geodesic metric on the unitary sphere. We show that this definition allows to adapt very simply classical image processing methods. We focus more particularly on image gradient estimation, interest point detection, and matching. More generally, the proposed approach extends traditional image processing techniques based on Euclidean metric to central catadioptric images. We show in this paper the efficiency of the approach through different experimental results and quantitative evaluations. [Display omitted] ► We propose a new definition of the neighborhood between pixels of a central catadioptric image. ► We show that this definition allows to use the same operators than in classical image processing techniques. ► Different experimental results on edge detection and interest point detection are presented and evaluated. ► Point matching methods derived from the new neighborhood definition are proposed.