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Two-View Orthographic Epipolar Geometry : Minimal and Optimal Solvers

Oskarsson, Magnus LU (2017) In Journal of Mathematical Imaging and Vision p.1-11
Abstract

We will in this paper present methods and algorithms for estimating two-view geometry based on an orthographic camera model. We use a previously neglected nonlinear criterion on rigidity to estimate the calibrated essential matrix. We give efficient algorithms for estimating it minimally (using only three point correspondences), in a least squares sense (using four or more point correspondences), and optimally with respect to the number of inliers. The inlier-optimal algorithm is based on a three-point solver and gives a fourth-order polynomial time algorithm. These methods can be used as building blocks to robustly find inlier correspondences in the presence of high degrees of outliers. We show experimentally that our methods can be... (More)

We will in this paper present methods and algorithms for estimating two-view geometry based on an orthographic camera model. We use a previously neglected nonlinear criterion on rigidity to estimate the calibrated essential matrix. We give efficient algorithms for estimating it minimally (using only three point correspondences), in a least squares sense (using four or more point correspondences), and optimally with respect to the number of inliers. The inlier-optimal algorithm is based on a three-point solver and gives a fourth-order polynomial time algorithm. These methods can be used as building blocks to robustly find inlier correspondences in the presence of high degrees of outliers. We show experimentally that our methods can be used in many instances, where the orthographic camera model isn’t generally used. A case of special interest is situations with repetitive structures, which give high amounts of outliers in the initial feature point matching.

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Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
in press
subject
in
Journal of Mathematical Imaging and Vision
pages
11 pages
publisher
Springer
external identifiers
  • scopus:85026905545
ISSN
0924-9907
DOI
10.1007/s10851-017-0753-1
language
English
LU publication?
yes
id
0b2d14df-4a11-4639-887a-de2ed8101e60
date added to LUP
2017-08-31 12:14:06
date last changed
2017-08-31 12:14:06
@article{0b2d14df-4a11-4639-887a-de2ed8101e60,
  abstract     = {<p>We will in this paper present methods and algorithms for estimating two-view geometry based on an orthographic camera model. We use a previously neglected nonlinear criterion on rigidity to estimate the calibrated essential matrix. We give efficient algorithms for estimating it minimally (using only three point correspondences), in a least squares sense (using four or more point correspondences), and optimally with respect to the number of inliers. The inlier-optimal algorithm is based on a three-point solver and gives a fourth-order polynomial time algorithm. These methods can be used as building blocks to robustly find inlier correspondences in the presence of high degrees of outliers. We show experimentally that our methods can be used in many instances, where the orthographic camera model isn’t generally used. A case of special interest is situations with repetitive structures, which give high amounts of outliers in the initial feature point matching.</p>},
  author       = {Oskarsson, Magnus},
  issn         = {0924-9907},
  language     = {eng},
  month        = {07},
  pages        = {1--11},
  publisher    = {Springer},
  series       = {Journal of Mathematical Imaging and Vision},
  title        = {Two-View Orthographic Epipolar Geometry : Minimal and Optimal Solvers},
  url          = {http://dx.doi.org/10.1007/s10851-017-0753-1},
  year         = {2017},
}