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

Luca Magri, Andrea Fusiello; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 7460-7468

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.

Related Material


[pdf]
[bibtex]
@InProceedings{Magri_2019_CVPR,
author = {Magri, Luca and Fusiello, Andrea},
title = {Fitting Multiple Heterogeneous Models by Multi-Class Cascaded T-Linkage},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}