Numerically Stable Optimization of Polynomial Solvers for Minimal Problems
Kuang, Yubin LU and Åström, Karl LU
(2012)
12th European Conference on Computer Vision (ECCV 2012)
7574.
p.100-113
- Abstract
- Numerous geometric problems in computer vision involve the solu- tion of systems of polynomial equations. This is particularly true for so called minimal problems, but also for finding stationary points for overdetermined prob- lems. The state-of-the-art is based on the use of numerical linear algebra on the large but sparse coefficient matrix that represents the original equations multi- plied with a set of monomials. The key observation in this paper is that the speed and numerical stability of the solver depends heavily on (i) what multiplication monomials are used and (ii) the set of so called permissible monomials from which numerical linear algebra routines choose the basis of a certain quotient ring. In the paper we show that... (More)
- Numerous geometric problems in computer vision involve the solu- tion of systems of polynomial equations. This is particularly true for so called minimal problems, but also for finding stationary points for overdetermined prob- lems. The state-of-the-art is based on the use of numerical linear algebra on the large but sparse coefficient matrix that represents the original equations multi- plied with a set of monomials. The key observation in this paper is that the speed and numerical stability of the solver depends heavily on (i) what multiplication monomials are used and (ii) the set of so called permissible monomials from which numerical linear algebra routines choose the basis of a certain quotient ring. In the paper we show that optimizing with respect to these two factors can give both significant improvements to numerical stability as compared to the state of the art, as well as highly compact solvers, while still retaining numerical stabil- ity. The methods are validated on several minimal problems that have previously been shown to be challenging (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/3424815
- author
- Kuang, Yubin
LU
and Åström, Karl
LU
- organization
- publishing date
- 2012
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- polynomial equations, computer vision, geometry
- host publication
- Lecture Notes in Computer Science (Computer Vision ECCV 2012, 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part III)
- editor
- Fitzgibbon, Andrew
- volume
- 7574
- pages
- 14 pages
- publisher
- Springer
- conference name
- 12th European Conference on Computer Vision (ECCV 2012)
- conference location
- Florence, Italy
- conference dates
- 2012-10-07 - 2012-10-13
- external identifiers
-
- scopus:84867876540
- ISSN
- 1611-3349
- 0302-9743
- ISBN
- 978-3-642-33711-6 (print)
- 3642337112
- 978-3-642-33712-3 (online)
- DOI
- 10.1007/978-3-642-33712-3_8
- language
- English
- LU publication?
- yes
- id
- eda580cc-9fbf-4bf2-b93f-081963907d39 (old id 3424815)
- alternative location
- http://link.springer.com/chapter/10.1007/978-3-642-33712-3_8
- date added to LUP
- 2016-04-01 11:11:59
- date last changed
- 2024-06-03 09:53:04
@inproceedings{eda580cc-9fbf-4bf2-b93f-081963907d39,
abstract = {{Numerous geometric problems in computer vision involve the solu- tion of systems of polynomial equations. This is particularly true for so called minimal problems, but also for finding stationary points for overdetermined prob- lems. The state-of-the-art is based on the use of numerical linear algebra on the large but sparse coefficient matrix that represents the original equations multi- plied with a set of monomials. The key observation in this paper is that the speed and numerical stability of the solver depends heavily on (i) what multiplication monomials are used and (ii) the set of so called permissible monomials from which numerical linear algebra routines choose the basis of a certain quotient ring. In the paper we show that optimizing with respect to these two factors can give both significant improvements to numerical stability as compared to the state of the art, as well as highly compact solvers, while still retaining numerical stabil- ity. The methods are validated on several minimal problems that have previously been shown to be challenging}},
author = {{Kuang, Yubin and Åström, Karl}},
booktitle = {{Lecture Notes in Computer Science (Computer Vision ECCV 2012, 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part III)}},
editor = {{Fitzgibbon, Andrew}},
isbn = {{978-3-642-33711-6 (print)}},
issn = {{1611-3349}},
keywords = {{polynomial equations; computer vision; geometry}},
language = {{eng}},
pages = {{100--113}},
publisher = {{Springer}},
title = {{Numerically Stable Optimization of Polynomial Solvers for Minimal Problems}},
url = {{http://dx.doi.org/10.1007/978-3-642-33712-3_8}},
doi = {{10.1007/978-3-642-33712-3_8}},
volume = {{7574}},
year = {{2012}},
}