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In computer vision, finding point correspondence among images plays animportant role in many applications, such as image stitching, image retrieval,visual localization, etc. Most of the research worksfocus on the matching oflocal feature before a sampling method is employed, such as RANSAC, to verifyinitial matching results via repeated fitting of certain global transformationamong the images. However, incorrect matches may still exist, while carefulexamination of such problems is often skipped. Accordingly, a geometricallyconstrained algorithm is proposed in this work to verify the correctness ofinitially matched SIFT keypoints based on view-invariant cross-ratios (CRs). Byrandomly forming pentagons from these keypoints and matching their shape andlocation among images with CRs, robust planar region estimation can be achievedefficiently for the above verification, while correct and incorrect matches ofkeypoints can be examined easily with respect to those shape and locationmatched pentagons. Experimental results show that satisfactory results can beobtained for various scenes with single as well as multiple planar regions.

A Geometrically Constrained Point Matching based on View-invariant Cross-ratios, and Homography