Affine invariant matching based on orientation estimation

In recent years several algorithms have been developed that allow feature matching methods to operate on images with large baseline variations such as Affine-Scale Invariant Feature Transform (ASIFT) and its variants. These algorithms solve the base line problem through simulating various potential transforms between image pairs. These simulated views may be easier to match using traditional feature matching algorithms than the original wide baseline views. This paper presents a novel approach to approximating the orientation between wide baseline views. The proposed method tentatively matches affine invariant regions, normalizes and aligns the regions using whitening transforms to produce an affine transform for the scene. To increase efficiency, edge pixels are utilized rather than correlating regions. Results show that the proposed method is able to match scenes containing up to 80 degrees in vertical and horizontal perspective change. The method is superior to state-of-the-art ASIFT algorithms in terms of execution time.