Matching Widely Separated Views Based on Affine Invariant Regions

TL;DR: To increase the robustness of the system, two semi-local constraints on combinations of region correspondences are derived (one geometric, the other photometric) allow to test the consistency of correspondences and hence to reject falsely matched regions.

Abstract: ‘Invariant regions’ are self-adaptive image patches that automatically deform with changing viewpoint as to keep on covering identical physical parts of a scene. Such regions can be extracted directly from a single image. They are then described by a set of invariant features, which makes it relatively easy to match them between views, even under wide baseline conditions. In this contribution, two methods to extract invariant regions are presented. The first one starts from corners and uses the nearby edges, while the second one is purely intensity-based. As a matter of fact, the goal is to build an opportunistic system that exploits several types of invariant regions as it sees fit. This yields more correspondences and a system that can deal with a wider range of images. To increase the robustness of the system, two semi-local constraints on combinations of region correspondences are derived (one geometric, the other photometric). They allow to test the consistency of correspondences and hence to reject falsely matched regions. Experiments on images of real-world scenes taken from substantially different viewpoints demonstrate the feasibility of the approach.