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Numerically Stable Optimization of Polynomial Solvers for Minimal Problems

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Author

Editor

  • Andrew Fitzgibbon

Summary, in English

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

Publishing year

2012

Language

English

Pages

100-113

Publication/Series

Lecture Notes in Computer Science (Computer Vision ECCV 2012, 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part III)

Volume

7574

Document type

Conference paper

Publisher

Springer

Topic

  • Mathematics

Keywords

  • polynomial equations
  • computer vision
  • geometry

Conference name

12th European Conference on Computer Vision (ECCV 2012)

Conference date

2012-10-07 - 2012-10-13

Conference place

Florence, Italy

Status

Published

Research group

  • Mathematical Imaging Group

ISBN/ISSN/Other

  • ISSN: 1611-3349
  • ISSN: 0302-9743
  • ISBN: 978-3-642-33711-6 (print)
  • ISBN: 3642337112
  • ISBN: 978-3-642-33712-3 (online)