Proceedings ArticleDOI
Robust detection of degenerate configurations for the fundamental matrix
Philip H. S. Torr,Andrew Zisserman,Stephen J. Maybank +2 more
- pp 1037-1042
TLDR
It is demonstrated that proper modelling of degeneracy in the presence of outlier enables the detection of outliers which would otherwise be missed.Abstract:
New methods are reported for the detection of multiple solutions (degeneracy) when estimating the fundamental matrix, with specific emphasis on robustness in the presence of data contamination (outliers). The fundamental matrix can be used as a first step in the recovery of structure from motion. If the set of correspondences is degenerate then this structure cannot be accurately recovered and many solutions will explain the data equally well. It is essential that we are alerted to such eventualities. However, current feature matchers are very prone to mismatching, giving a high rate of contamination within the data. Such contamination can make a degenerate data set appear non degenerate, thus the need for robust methods becomes apparent. The paper presents such methods with a particular emphasis on providing a method that will work on real imagery and with an automated (non perfect) feature detector and matcher. It is demonstrated that proper modelling of degeneracy in the presence of outliers enables the detection of outliers which would otherwise be missed. Results using real image sequences are presented. All processing, point matching, degeneracy detection and outlier detection is automatic. >read more
Citations
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Journal ArticleDOI
MLESAC: A New Robust Estimator with Application to Estimating Image Geometry
TL;DR: A new robust estimator MLESAC is presented which is a generalization of the RANSAC estimator which adopts the same sampling strategy as RANSac to generate putative solutions, but chooses the solution that maximizes the likelihood rather than just the number of inliers.
Journal ArticleDOI
Determining the Epipolar Geometry and its Uncertainty: A Review
TL;DR: A complete review of the current techniques for estimating the fundamental matrix and its uncertainty is provided, and a well-founded measure is proposed to compare these techniques.
Journal ArticleDOI
The Development and Comparison of Robust Methodsfor Estimating the Fundamental Matrix
TL;DR: A variety of robust methods for the computation of the Fundamental Matrix, the calibration-free representation of camera motion, are developed from the principal categories of robust estimators, viz. case deletion diagnostics, M-estimators and random sampling, and the theory required to apply them to non-linear orthogonal regression problems is developed.
An Image-Based Approach to Three-Dimensional Computer Graphics
TL;DR: This research derives an image-warping equation that maps the visible points in a reference image to their correct positions in any desired view and derives a new visibility algorithm that determines a drawing order for the image warp.
Journal ArticleDOI
A unified approach to moving object detection in 2D and 3D scenes
Michal Irani,Padmanabhan Anandan +1 more
TL;DR: A unified approach to handling moving object detection in both 2D and 3D scenes is described, with a strategy to gracefully bridge the gap between those two extremes, based on a stratification of theMoving object detection problem into scenarios which gradually increase in their complexity.
References
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