Consistency of robust estimators in multi-structural visual data segmentation
Pattern Recognition
Instantaneous 3D motion from image derivatives using the Least Trimmed Square regression
Pattern Recognition Letters
A consensus sampling technique for fast and robust model fitting
Pattern Recognition
Adaptive Sample Consensus for Efficient Random Optimization
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
An adaptive-scale robust estimator for motion estimation
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Journal of Mathematical Imaging and Vision
Robust Algebraic Segmentation of Mixed Rigid-Body and Planar Motions from Two Views
International Journal of Computer Vision
Rejecting Mismatches by Correspondence Function
International Journal of Computer Vision
Hill climbing algorithm for random sample consensus methods
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part I
An M-estimator for high breakdown robust estimation in computer vision
Computer Vision and Image Understanding
Adaptive-Scale robust estimator using distribution model fitting
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Efficient and robust model fitting with unknown noise scale
Image and Vision Computing
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RANSAC is the most widely used robust regression algorithm in computer vision. However, RANSAC has a few drawbacks which make it difficult to use in a lot of applications. Some of these problems have been addressed through improved sampling algorithms or better cost functions, but an important problem still remains. The algorithms are not user independent, and require some knowledge of the scale of the inlier noise. The projection based M-estimator (pbM) offers a solution to this by reframing the regression problem in a projection pursuit framework. In this paper we derive the pbM algorithm for heteroscedastic data. Our algorithm is applied to various real problems and its performance is compared with RANSAC and MSAC. It is shown that pbM gives better results than RANSAC and MSAC in spite of being user independent.