Robust factorization
Aanaes, H ; Fisker, R ; Åström, Karl LU
and Carstensen, JM
(2002)
In IEEE Transactions on Pattern Analysis and Machine Intelligence
24(9).
p.1215-1225
- Abstract
- Factorization algorithms for recovering structure and motion from an image stream have many advantages, but they usually require a set of well-tracked features. Such a set is in generally not available in practical applications. There is thus a need for making factorization algorithms deal effectively with errors in the tracked features. We propose a new and computationally efficient algorithm for applying an arbitrary errorfunction in the factorization scheme. This algorithm enables the use of robust statistical techniques and arbitrary noise models for the individual features. These techniques and models enable the factorization scheme to deal effectively with mismatched features, missing features, and noise on the individual features.... (More)
- Factorization algorithms for recovering structure and motion from an image stream have many advantages, but they usually require a set of well-tracked features. Such a set is in generally not available in practical applications. There is thus a need for making factorization algorithms deal effectively with errors in the tracked features. We propose a new and computationally efficient algorithm for applying an arbitrary errorfunction in the factorization scheme. This algorithm enables the use of robust statistical techniques and arbitrary noise models for the individual features. These techniques and models enable the factorization scheme to deal effectively with mismatched features, missing features, and noise on the individual features. The proposed approach further includes a new method for Euclidean reconstruction that significantly improves convergence of the factorization algorithms. The proposed algorithm has been implemented as a modification of the Christy-Horaud factorization scheme, which yields a perspective reconstruction. Based on this implementation, a considerable increase in error tolerance is demonstrated on real and synthetic data. The proposed scheme can, however, be applied to most other factorization algorithms. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/330076
- author
- Aanaes, H
; Fisker, R
; Åström, Karl
LU
and Carstensen, JM
- organization
- publishing date
- 2002
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- structure from motion, Euclidean reconstruction, perspective reconstruction, robust statistics, feature tracking
- in
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- volume
- 24
- issue
- 9
- pages
- 1215 - 1225
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- external identifiers
-
- wos:000177640500005
- scopus:0036709180
- ISSN
- 1939-3539
- DOI
- 10.1109/TPAMI.2002.1033213
- language
- English
- LU publication?
- yes
- id
- 2d099cf6-85e0-40e2-820d-6005016ce94b (old id 330076)
- date added to LUP
- 2016-04-01 17:03:52
- date last changed
- 2022-04-07 20:37:13
@article{2d099cf6-85e0-40e2-820d-6005016ce94b,
abstract = {{Factorization algorithms for recovering structure and motion from an image stream have many advantages, but they usually require a set of well-tracked features. Such a set is in generally not available in practical applications. There is thus a need for making factorization algorithms deal effectively with errors in the tracked features. We propose a new and computationally efficient algorithm for applying an arbitrary errorfunction in the factorization scheme. This algorithm enables the use of robust statistical techniques and arbitrary noise models for the individual features. These techniques and models enable the factorization scheme to deal effectively with mismatched features, missing features, and noise on the individual features. The proposed approach further includes a new method for Euclidean reconstruction that significantly improves convergence of the factorization algorithms. The proposed algorithm has been implemented as a modification of the Christy-Horaud factorization scheme, which yields a perspective reconstruction. Based on this implementation, a considerable increase in error tolerance is demonstrated on real and synthetic data. The proposed scheme can, however, be applied to most other factorization algorithms.}},
author = {{Aanaes, H and Fisker, R and Åström, Karl and Carstensen, JM}},
issn = {{1939-3539}},
keywords = {{structure from motion; Euclidean reconstruction; perspective reconstruction; robust statistics; feature tracking}},
language = {{eng}},
number = {{9}},
pages = {{1215--1225}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{IEEE Transactions on Pattern Analysis and Machine Intelligence}},
title = {{Robust factorization}},
url = {{http://dx.doi.org/10.1109/TPAMI.2002.1033213}},
doi = {{10.1109/TPAMI.2002.1033213}},
volume = {{24}},
year = {{2002}},
}