Efficient Robust Mean Value Calculation of 1D Features
Computer Vision and Pattern Recognition
2016-02-01 v1
Abstract
A robust mean value is often a good alternative to the standard mean value when dealing with data containing many outliers. An efficient method for samples of one-dimensional features and the truncated quadratic error norm is presented and compared to the method of channel averaging (soft histograms).
Cite
@article{arxiv.1601.08003,
title = {Efficient Robust Mean Value Calculation of 1D Features},
author = {Erik Jonsson and Michael Felsberg},
journal= {arXiv preprint arXiv:1601.08003},
year = {2016}
}
Comments
Presented at the SSBA Symposium 2005, Malm\"o, Sweden