NDLI: Fast and robust homography estimation method with algebraic outlier rejection

Content Provider	IET Digital Library
Author	Qi, Naixin Zhang, Shengxiu Cao, Lijia Yang, Xiaogang Li, Chuanxiang He, Chuan
Abstract	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.
Starting Page	552
Ending Page	562
Page Count	11
ISSN	17519659
Volume Number	12
e-ISSN	17519667
Issue Number	Issue 4, Apr (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/12/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.0254
Journal	IET Image Processing
Publisher Date	2017-11-22
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Algebra Algebraic Error Characteristic Algebraic Outlier Rejection Computational Complexity Computer Vision And Image Processing Technique Estimation Pipeline Estimation Theory Homography Estimation Method Image Processing Image Transformation Degree Minimisation Objective Quality Measurement Optical, Image And Video Signal Processing Optimisation Technique Random Processes Random Sample Consensus Algorithm Reprojection Error Residual Vector Minimisation Statistics Subjective Visual Quality Vector
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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