Geometric computation for machine vision
Geometric computation for machine vision
Statistical analysis of geometric computation
CVGIP: Image Understanding
Robust detection of degenerate configurations while estimating the fundamental matrix
Computer Vision and Image Understanding
The Problem of Degeneracy in Structure and Motion Recovery from Uncalibrated Image Sequences
International Journal of Computer Vision - 1998 Marr Prize
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
A Bayesian Method for Fitting Parametric and Nonparametric Models to Noisy Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Bayesian Model Estimation and Selection for Epipolar Geometry and Generic Manifold Fitting
International Journal of Computer Vision
Balanced Recovery of 3D Structure and Camera Motion from Uncalibrated Image Sequences
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Multi-View Subspace Constraints on Homographies
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Recovery of Epipolar Geometry as a Manifold Fitting Problem
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Motion Recovery by Integrating over the Joint Image Manifold
International Journal of Computer Vision
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The Bayesian method is widely used in image processing and computer vision to solve ill-posed problems. This is commonly achieved by introducing a prior which, together with the data constraints, determines a unique and hopefully stable solution. Choosing a "correct" prior is however a well-known obstacle.This paper demonstrates that in a certain class of motion estimation problems, the Bayesian technique of integrating out the "nuisance parameters" yields stable solutions even if a flat prior on the motion parameters is used. The advantage of the suggested method is more noticeable when the domain points approach a degenerate configuration, and/or when the noise is relatively large with respect to the size of the point configuration.