ML Anthology

Fitting Multiple Heterogeneous Models by Multi-Class Cascaded T-Linkage

Luca Magri, Andrea Fusiello
CVPR 2019
doi:10.1109/CVPR.2019.00764 /cvpr/2019/magri2019cvpr-fitting/

Abstract

This paper addresses the problem of multiple models fitting in the general context where the sought structures can be described by a mixture of heterogeneous parametric models drawn from different classes. To this end, we conceive a multi-model selection framework that extend T-linkage to cope with different nested class of models. Our method, called MCT, compares favourably with the state-of-the-art on publicly available data-sets for various fitting problems: lines and conics, homographies and fundamental matrices, planes and cylinders.

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Cite

Text

Magri and Fusiello. "Fitting Multiple Heterogeneous Models by Multi-Class Cascaded T-Linkage." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019. doi:10.1109/CVPR.2019.00764

Markdown

[Magri and Fusiello. "Fitting Multiple Heterogeneous Models by Multi-Class Cascaded T-Linkage." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019.](https://mlanthology.org/cvpr/2019/magri2019cvpr-fitting/) doi:10.1109/CVPR.2019.00764

BibTeX

@inproceedings{magri2019cvpr-fitting,
  title     = {{Fitting Multiple Heterogeneous Models by Multi-Class Cascaded T-Linkage}},
  author    = {Magri, Luca and Fusiello, Andrea},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2019},
  doi       = {10.1109/CVPR.2019.00764},
  url       = {https://mlanthology.org/cvpr/2019/magri2019cvpr-fitting/}
}