Open AccessFast and robust homography estimation method with algebraic outlier rejection
Author(s) -
Qi Naixin,
Zhang Shengxiu,
Cao Lijia,
Yang Xiaogang,
Li Chuanxiang,
He Chuan
Publication year - 2018
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2017.0254
Subject(s) - outlier , homography , computer science , artificial intelligence , ransac , anomaly detection , pattern recognition (psychology) , computational complexity theory , residual , projection (relational algebra) , transformation (genetics) , algorithm , mathematics , image (mathematics) , statistics , biochemistry , chemistry , projective test , projective space , gene
To solve the homography estimation problem containing outliers and noise, a fast, robust, and accurate method is proposed. In this method, the outliers are rejected based on the differing characteristics of algebraic errors between outliers and inliers, and the homography is estimated by minimising the residual vector. The advantage of this method is in integrating the outlier rejection into the estimation pipeline. The computational complexity of the proposed method is not increased, and the random sample consensus algorithm is not needed to extract the inliers, as was previously necessary. Since the outlier rejection process is based on an algebraic criterion without computing the re‐projection error at each step, the speed of the proposed method is improved. Several simulations based on synthetic and real images illustrate the performance of the proposed method in terms of subjective visual quality, objective quality measurement, and computational time. The experimental results demonstrate that the proposed method achieves accurate, efficient and robust homography estimation under different image transformation degrees and different outlier ratios.