Multiple Model Fitting as a Set Coverage Problem
Abstract
This paper deals with the extraction of multiple models from noisy or outlier-contaminated data. We cast the multi-model fitting problem in terms of set covering, deriving a simple and effective method that generalizes Ransac to multiple models and deals with intersecting structures and outliers in a straightforward and principled manner, while avoiding the typical shortcomings of sequential approaches and those of clustering. The method compares favourably against the state-of-the-art on simulated and publicly available real datasets.
Cite
Text
Magri and Fusiello. "Multiple Model Fitting as a Set Coverage Problem." Conference on Computer Vision and Pattern Recognition, 2016.Markdown
[Magri and Fusiello. "Multiple Model Fitting as a Set Coverage Problem." Conference on Computer Vision and Pattern Recognition, 2016.](https://mlanthology.org/cvpr/2016/magri2016cvpr-multiple/)BibTeX
@inproceedings{magri2016cvpr-multiple,
title = {{Multiple Model Fitting as a Set Coverage Problem}},
author = {Magri, Luca and Fusiello, Andrea},
booktitle = {Conference on Computer Vision and Pattern Recognition},
year = {2016},
url = {https://mlanthology.org/cvpr/2016/magri2016cvpr-multiple/}
}