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T-Linkage: A Continuous Relaxation of J-Linkage for Multi-Model Fitting
AbstractThis 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.
Related Material
[pdf][bibtex]@InProceedings{Magri_2014_CVPR,
author = {Magri, Luca and Fusiello, Andrea},
title = {T-Linkage: A Continuous Relaxation of J-Linkage for Multi-Model Fitting},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2014}
}
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