Robust Statistical Estimation and Segmentation of Multiple Subspaces
Abstract
We study the problem of estimating a mixed geometric model of multiple subspaces in the presence of a significant amount of outliers. The estimation of multiple subspaces is an important problem in computer vision, particularly for segmenting multiple motions in an image sequence. We first provide a comprehensive survey of robust statistical techniques in the literature, and identify three main approaches for detecting and rejecting outliers. Through a careful examination of these approaches, we propose and investigate three principled methods for robustly estimating mixed subspace models: random sample consensus, the influence function, and multivariate trimming. Using a benchmark synthetic experiment and a set of real image sequences, we conduct a thorough comparison of the three methods
Cite
Text
Yang et al. "Robust Statistical Estimation and Segmentation of Multiple Subspaces." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2006. doi:10.1109/CVPRW.2006.178Markdown
[Yang et al. "Robust Statistical Estimation and Segmentation of Multiple Subspaces." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2006.](https://mlanthology.org/cvprw/2006/yang2006cvprw-robust/) doi:10.1109/CVPRW.2006.178BibTeX
@inproceedings{yang2006cvprw-robust,
title = {{Robust Statistical Estimation and Segmentation of Multiple Subspaces}},
author = {Yang, Allen Y. and Rao, Shankar R. and Ma, Yi},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2006},
pages = {99},
doi = {10.1109/CVPRW.2006.178},
url = {https://mlanthology.org/cvprw/2006/yang2006cvprw-robust/}
}