Paper Info
Title
A Robust Method for Estimating Image Geometry With Local Structure Constraint.
Abstract
The estimation of image geometry benefits many applications in the field of computer vision, such as stereo correspondence, 3-D reconstruction, and camera self-calibration. It is very challenging since the proportion of inliers in putative correspondences is usually very low, and traditional image geometry estimation methods (such as Ransac) suffer from low accuracy at a high outlier ratio. In this paper, we tackle the two-view image geometry estimation problem and propose a new robust estimation method L2E-LSC (short for L2E with local structure constraint) based on the L2E algorithm. In particular, we first establish initial correspondences by feature description matches, and then estimate the fundamental matrix and homography using L2E-LSC and get the refined correspondences. The L2E-LSC is able to robustly deal with the noise and outliers contained in point correspondences. Extensive experiments conducted on real images from public available datasets have demonstrated that it can achieve good estimation accuracy and superior performance over previous approaches, particularly when there are severe outliers.
Year
DOI
Venue
2018
10.1109/ACCESS.2018.2803152
IEEE ACCESS
Keywords
Field
DocType
Robust estimator,multiple view relation,fundamental matrix,homography
Data modeling,RANSAC,Computer science,Local structure,Outlier,Robustness (computer science),Homography,Real image,Geometry,Fundamental matrix (computer vision)
Journal
Volume
ISSN
Citations 
6
2169-3536
4
PageRank 
References 
Authors
0.38
0
4
Name
Order
Citations
PageRank
Li Peng194.03
Yanduo Zhang23912.01
Huabing Zhou321615.18
Tao Lu414926.63