Robust Multiple Structures Estimation with J-Linkage
Abstract
This paper tackles the problem of fitting multiple instances of a model to data corrupted by noise and outliers. The proposed solution is based on random sampling and conceptual data representation. Each point is represented with the characteristic function of the set of random models that fit the point. A tailored agglomerative clustering, called J-linkage, is used to group points belonging to the same model. The method does not require prior specification of the number of models, nor it necessitate parameters tuning. Experimental results demonstrate the superior performances of the algorithm.
Cite
Text
Toldo and Fusiello. "Robust Multiple Structures Estimation with J-Linkage." European Conference on Computer Vision, 2008. doi:10.1007/978-3-540-88682-2_41Markdown
[Toldo and Fusiello. "Robust Multiple Structures Estimation with J-Linkage." European Conference on Computer Vision, 2008.](https://mlanthology.org/eccv/2008/toldo2008eccv-robust/) doi:10.1007/978-3-540-88682-2_41BibTeX
@inproceedings{toldo2008eccv-robust,
title = {{Robust Multiple Structures Estimation with J-Linkage}},
author = {Toldo, Roberto and Fusiello, Andrea},
booktitle = {European Conference on Computer Vision},
year = {2008},
pages = {537-547},
doi = {10.1007/978-3-540-88682-2_41},
url = {https://mlanthology.org/eccv/2008/toldo2008eccv-robust/}
}