A Geometrically Constrained Point Matching based on View-invariant
Cross-ratios, and Homography
- URL: http://arxiv.org/abs/2211.03007v1
- Date: Sun, 6 Nov 2022 01:55:35 GMT
- Title: A Geometrically Constrained Point Matching based on View-invariant
Cross-ratios, and Homography
- Authors: Yueh-Cheng Huang, Ching-Huai Yang, Chen-Tao Hsu, and Jen-Hui Chuang
- Abstract summary: A geometrically constrained algorithm is proposed 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.
Experimental results show that satisfactory results can be obtained for various scenes with single as well as multiple planar regions.
- Score: 2.050924050557755
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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