Estimation of epipolar geometry by linear mixed-effect modelling

Authors:
Huiyu Zhou;Patrick R. Green;Andrew M. Wallace
Affiliations:
School of Engineering and Design, Brunel University, UB8 3PH, UK;School of Life Sciences, Heriot-Watt University, EH14 4AS, UK;School of Engineering and Physical Sciences, Heriot-Watt University, EH14 4AS, UK
Venue:
Neurocomputing
Year:
2009

Citing 14
Cited 5

Robust regression and outlier detection

Robust regression and outlier detection
Motion and Structure From Two Perspective Views: Algorithms, Error Analysis, and Error Estimation

IEEE Transactions on Pattern Analysis and Machine Intelligence
A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry

Artificial Intelligence - Special volume on computer vision
The Development and Comparison of Robust Methodsfor Estimating the Fundamental Matrix

International Journal of Computer Vision
MLESAC: a new robust estimator with application to estimating image geometry

Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography

Communications of the ACM
Multiple view geometry in computer visiond

Multiple view geometry in computer visiond
Estimating the Fundamental Matrix via Constrained Least-Squares: A Convex Approach

IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision

Computer Vision
In defence of the 8-point algorithm

ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
A Probabilistic Criterion to Detect Rigid Point Matches Between Two Images and Estimate the Fundamental Matrix

International Journal of Computer Vision
Nonlinear Estimation of the Fundamental Matrix with Minimal Parameters

IEEE Transactions on Pattern Analysis and Machine Intelligence
Epipolar geometry estimation based on evolutionary agents

Pattern Recognition
On the probabilistic epipolar geometry

Image and Vision Computing

New similarity measures of localities for a two-layer matching scheme and estimation of fundamental matrices

Neurocomputing
Ellipse constraints for improved wide-baseline feature matching and reconstruction

IWCIA'11 Proceedings of the 14th international conference on Combinatorial image analysis
Estimation of F-Matrix and image rectification by double quaternion

Information Sciences: an International Journal
Projection ray intersecting location-based multicolour pseudo-random coded projected active vision method

International Journal of Computer Applications in Technology
Estimating the fundamental matrix based on least absolute deviation

Neurocomputing

Quantified Score

Hi-index	0.01

Visualization

Abstract

Epipolar geometry relies on the determination of the fundamental matrix. Classical approaches for estimating the fundamental matrix assume that a Gaussian distribution exists in the errors in view of mathematical tractability. However, this assumption will not be justified when the distribution computed is not normally distributed. We propose a new approach that does not make the Gaussian assumption, and so can attain robustness and accuracy in different conditions. The proposed framework, weighted least squares (WLS), is the application of linear mixed-effect models considering the correlation between different data subsamples. It provides an unbiased estimation of the fundamental matrix after mitigating the effects of outliers. We test the new model by using synthetic and real images, and comparing it to standard methods.