Bias in Robust Estimation Caused by Discontinuities and Multiple Structures
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Robust Estimation for Range Image Segmentation and Reconstruction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Adaptive-Scale Parametric Model Estimation for Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Consistency of robust estimators in multi-structural visual data segmentation
Pattern Recognition
Aerial tracking of elongated objects in rural environments
Machine Vision and Applications
Range segmentation of large building exteriors: A hierarchical robust approach
Computer Vision and Image Understanding
A novel hierarchical technique for range segmentation of large building exteriors
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
An M-estimator for high breakdown robust estimation in computer vision
Computer Vision and Image Understanding
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In computer vision applications of robust estimation techniques, it is usually assumed that a large number of data samples are available. As a result, the finite sample bias of estimation processes has been overlooked. This is despite the fact that many asymptotically unbiased estimators have substantial bias in cases where a moderate number of data samples are available. Such cases are frequently encountered in computer vision practice, therefore, it is important to choose the right estimator for a given task by virtue of knowing its finite sample bias. This paper investigates the finite sample bias of robust scale estimation and analyses the finite sample performance of three modern robust scale estimators (Modified Statistical Scale Estimator, Residual Consensus estimator and Two-Step Scale Estimator) that have been used in computer vision applications. Simulations and real data experiments are used to verify the results.