Computer vision theory: The lack thereof
Computer Vision, Graphics, and Image Processing
Pattern Recognition
Scene Segmentation from Visual Motion Using Global Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust regression and outlier detection
Robust regression and outlier detection
A simplification to linear two-view motion algorithms
Computer Vision, Graphics, and Image Processing
Machine Vision for Inspection and Measurement
Machine Vision for Inspection and Measurement
Robust Reweighted MAP Motion Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Estimation of Rotation Angles from Image Sequences Usingthe Annealing M-Estimator
Journal of Mathematical Imaging and Vision
Determining the Epipolar Geometry and its Uncertainty: A Review
International Journal of Computer Vision
A Robust Competitive Clustering Algorithm With Applications in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Method for Mining Regression Classes in Large Data Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust 3-D-3-D Pose Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
MINPRAN: A New Robust Estimator for Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optic Flow Field Segmentation and Motion Estimation Using a Robust Genetic Partitioning Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Similarity-Based Robust Clustering Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
A highly robust estimator for regression models
Pattern Recognition Letters
Alternative learning vector quantization
Pattern Recognition
Pattern Recognition
A revision for gaussian mixture density decomposition algorithm
CIS'04 Proceedings of the First international conference on Computational and Information Science
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The authors present a highly robust estimator, known as the model fitting (MF) estimator for general regression. They explain that high robustness becomes possible through partially but completely modeling the unknown log likelihood function. The partial modeling takes place by taking the Bayesian statistical decision rule and a number of important heuristics into consideration while maximizing the log likelihood function. Applications include the automatic selection of multiple thresholds, single rigid motion estimation or multiple rigid motion segmentation, and estimation from two perspective views. It is believed that the proposed MF estimator will aid in solving many robust estimation problems that demand an estimator that is either highly robust or capable of handling contaminated Gaussian mixture models.