A Convex Optimization Approach to Robust Fundamental Matrix Estimation

Yongfang Cheng, Jose A. Lopez, Octavia Camps, Mario Sznaier

CVPR 2015

doi:10.1109/CVPR.2015.7298829 /cvpr/2015/cheng2015cvpr-convex/

Abstract

This paper considers the problem of recovering a subspace arrangement from noisy samples, potentially corrupted with outliers. Our main result shows that this problem can be formulated as a constrained polynomial optimization, for which a monotonically convergent sequence of tractable convex relaxations can be obtained by exploiting recent developments in sparse polynomial optimization. Further, these results allow for deriving conditions certifying that a finite order relaxation has converged to a solution. A salient feature of the proposed approach is its ability to incorporate existing a-priori information about the noise, co-ocurrences, and percentage of outliers. These results are illustrated with several examples where the proposed algorithm is shown to outperform existing approaches.

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Cite

Text

Cheng et al. "A Convex Optimization Approach to Robust Fundamental Matrix Estimation." Conference on Computer Vision and Pattern Recognition, 2015. doi:10.1109/CVPR.2015.7298829

Markdown

[Cheng et al. "A Convex Optimization Approach to Robust Fundamental Matrix Estimation." Conference on Computer Vision and Pattern Recognition, 2015.](https://mlanthology.org/cvpr/2015/cheng2015cvpr-convex/) doi:10.1109/CVPR.2015.7298829

BibTeX

@inproceedings{cheng2015cvpr-convex,
  title     = {{A Convex Optimization Approach to Robust Fundamental Matrix Estimation}},
  author    = {Cheng, Yongfang and Lopez, Jose A. and Camps, Octavia and Sznaier, Mario},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2015},
  doi       = {10.1109/CVPR.2015.7298829},
  url       = {https://mlanthology.org/cvpr/2015/cheng2015cvpr-convex/}
}