T-Linkage: A Continuous Relaxation of J-Linkage for Multi-Model Fitting

CVPR 2014

doi:10.1109/CVPR.2014.505 /cvpr/2014/magri2014cvpr-tlinkage/

Abstract

This paper presents an improvement of the J-linkage algorithm for fitting multiple instances of a model to noisy data corrupted by outliers. The binary preference analysis implemented by J-linkage is replaced by a continuous (soft, or fuzzy) generalization that proves to perform better than J-linkage on simulated data, and compares favorably with state of the art methods on public domain real datasets.

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Cite

Text

Magri and Fusiello. "T-Linkage: A Continuous Relaxation of J-Linkage for Multi-Model Fitting." Conference on Computer Vision and Pattern Recognition, 2014. doi:10.1109/CVPR.2014.505

Markdown

[Magri and Fusiello. "T-Linkage: A Continuous Relaxation of J-Linkage for Multi-Model Fitting." Conference on Computer Vision and Pattern Recognition, 2014.](https://mlanthology.org/cvpr/2014/magri2014cvpr-tlinkage/) doi:10.1109/CVPR.2014.505

BibTeX

@inproceedings{magri2014cvpr-tlinkage,
  title     = {{T-Linkage: A Continuous Relaxation of J-Linkage for Multi-Model Fitting}},
  author    = {Magri, Luca and Fusiello, Andrea},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2014},
  doi       = {10.1109/CVPR.2014.505},
  url       = {https://mlanthology.org/cvpr/2014/magri2014cvpr-tlinkage/}
}