Tractable Algorithms for Robust Model Estimation
Författare
Summary, in English
What is the computational complexity of geometric model estimation in the presence of noise and outliers? We show that the number of outliers can be minimized in polynomial time with respect to the number of measurements, although exponential in the model dimension. Moreover, for a large class of problems, we prove that the statistically more desirable truncated L2-norm can be optimized with the same complexity. In a similar vein, it is also shown how to transform a multi-model estimation problem into a purely combinatorial one—with worst-case complexity that is polynomial in the number of measurements but exponential in the number of models. We apply our framework to a series of hard fitting problems. It gives a practical method for simultaneously dealing with measurement noise and large amounts of outliers in the estimation of low-dimensional models. Experimental results and a comparison to random sampling techniques are presented for the applications rigid registration, triangulation and stitching.
Avdelning/ar
Publiceringsår
2015
Språk
Engelska
Sidor
115-129
Publikation/Tidskrift/Serie
International Journal of Computer Vision
Volym
112
Issue
1
Dokumenttyp
Artikel i tidskrift
Förlag
Springer
Ämne
- Mathematics
Nyckelord
- Outliers Geometry Optimization 3D reconstruction Image registration
Status
Published
Forskningsgrupp
- Mathematical Imaging Group
ISBN/ISSN/Övrigt
- ISSN: 1573-1405