Multiple Model Fitting as a Set Coverage Problem

Luca Magri, Andrea Fusiello; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 3318-3326

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.

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[bibtex]
@InProceedings{Magri_2016_CVPR,
author = {Magri, Luca and Fusiello, Andrea},
title = {Multiple Model Fitting as a Set Coverage Problem},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2016}
}