A Geometrically Constrained Point Matching based on View-invariant Cross-ratios, and Homography
This work addresses a specific bottleneck in image matching for applications like stitching and localization, offering an incremental improvement over existing methods.
The paper tackles the problem of incorrect point correspondences in computer vision by proposing a geometrically constrained algorithm that verifies initially matched SIFT keypoints using view-invariant cross-ratios, achieving robust planar region estimation and effective examination of correct and incorrect matches for various scenes.
In computer vision, finding point correspondence among images plays an important role in many applications, such as image stitching, image retrieval, visual localization, etc. Most of the research worksfocus on the matching of local feature before a sampling method is employed, such as RANSAC, to verify initial matching results via repeated fitting of certain global transformation among the images. However, incorrect matches may still exist, while careful examination of such problems is often skipped. Accordingly, a geometrically constrained algorithm is proposed in this work to verify the correctness of initially matched SIFT keypoints based on view-invariant cross-ratios (CRs). By randomly forming pentagons from these keypoints and matching their shape and location among images with CRs, robust planar region estimation can be achieved efficiently for the above verification, while correct and incorrect matches of keypoints can be examined easily with respect to those shape and location matched pentagons. Experimental results show that satisfactory results can be obtained for various scenes with single as well as multiple planar regions.