Akaike information criterion statistics
Akaike information criterion statistics
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 Classification EM algorithm for clustering and two stochastic versions
Computational Statistics & Data Analysis - Special issue on optimization techniques in statistics
The nature of statistical learning theory
The nature of statistical learning theory
Introduction to statistical signal processing with applications
Introduction to statistical signal processing with applications
Artificial Intelligence - Special volume on computer vision
The Development and Comparison of Robust Methodsfor Estimating the Fundamental Matrix
International Journal of Computer Vision
Characterizing the uncertainty of the fundamental matrix
Computer Vision and Image Understanding
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
A Bayesian weighting principle for the fundamental matrix estimation
Pattern Recognition Letters
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Estimating the fundamental matrix by transforming image points in projective space
Computer Vision and Image Understanding
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
Epipolar Geometry in Stereo, Motion, and Object Recognition: A Unified Approach
Epipolar Geometry in Stereo, Motion, and Object Recognition: A Unified Approach
Motion From Point Matches Using Affine Epipolar Geometry
ECCV '94 Proceedings of the Third European Conference-Volume II on Computer Vision - Volume II
On the probabilistic epipolar geometry
Image and Vision Computing
The mixtures of Student's t-distributions as a robust framework for rigid registration
Image and Vision Computing
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In this paper, we will consider a robust estimator, which was proposed earlier by the authors, in a general non-linear regression framework. The basic idea of the estimator is, instead of trying to classify the observations to good and false, to model the residual distribution of the contaminants, determine the probability for each observation to be a good sample, and finally perform weighted fitting. The main contributions of this paper are: (1) We show now that the estimator is consistent with the true parameter values that simply means optimality regardless of the problematical outliers in the observations. (2) We propose how robust uncertainty computations and robust model selection can be performed in the similar, consistent manner. (3) We derive the expectation maximisation algorithm for the estimator and (4) extend the estimator to handle unknown outlier residual distributions. (5) We finally give some experiments with real data, where robustness in model fitting is needed.