A stable and efficient algorithm for nonlinear orthogonal distance regression
SIAM Journal on Scientific and Statistical Computing
Robust regression and outlier detection
Robust regression and outlier detection
Robust regression methods for computer vision: a review
International Journal of Computer Vision
The Development and Comparison of Robust Methodsfor Estimating the Fundamental Matrix
International Journal of Computer Vision
Constructive fitting and extraction of geometric primitives
Graphical Models and Image Processing
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Geometric Primitive Extraction Using a Genetic Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
MINPRAN: A New Robust Estimator for Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Approach to Outlier Detection Based on Bayesian Probabilistic Model
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
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Robust parameter estimation methods have become very popular in the computer vision community. Nevertheless, both optimization models and resolution algorithms coming from robust statistics must be adapted to correctly tackle the specificities of visual data. Among these adapted techniques, computer-vision researchers frequently use bucket-based partitions of the data (bucketing techniques). This work points out the key ideas and features of bucketing techniques. A new stochastic sampling scheme is proposed and defended. We also try to answer several questions, which are generally -and perhaps voluntarily-bypassed : "does the bucketing strategy influence the regression process ?" ; " how should the data be split into buckets to get the best fits both numerically and physically ?"...