Trust No One: Low Rank Matrix Factorization Using Hierarchical RANSAC
Oskarsson, Magnus LU
; Batstone, Kenneth
LU
and Åström, Kalle
LU
(2016)
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
p.5820-5829
- Abstract
- In this paper we present a system for performing low rank matrix factorization. Low-rank matrix factorization is an essential problem in many areas including computer vision, with applications in e.g. affine structure-from-motion, photometric stereo, and non-rigid structure from motion. We specifically target structured data patterns, with outliers and large amounts of missing data. Using recently developed characterizations of minimal solutions to matrix factorization problems with missing data, we show how these can be used as building blocks in a hierarchical system that performs bootstrapping on all levels. This gives an robust and fast system, with state-of-the-art performance.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/7f903c05-7cc0-4b65-9922-a65dc6f60e68
- author
- Oskarsson, Magnus
LU
; Batstone, Kenneth
LU
and Åström, Kalle
LU
- organization
- publishing date
- 2016-06-01
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), proceedings of
- pages
- 10 pages
- publisher
- Computer Vision Foundation
- conference name
- 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- conference location
- Seattle, United States
- conference dates
- 2016-06-27 - 2016-06-30
- external identifiers
-
- scopus:85063608901
- project
- Semantic Mapping and Visual Navigation for Smart Robots
- language
- English
- LU publication?
- yes
- id
- 7f903c05-7cc0-4b65-9922-a65dc6f60e68
- alternative location
- http://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Oskarsson_Trust_No_One_CVPR_2016_paper.html
- date added to LUP
- 2016-09-05 10:28:40
- date last changed
- 2022-05-10 00:41:00
@inproceedings{7f903c05-7cc0-4b65-9922-a65dc6f60e68,
abstract = {{In this paper we present a system for performing low rank matrix factorization. Low-rank matrix factorization is an essential problem in many areas including computer vision, with applications in e.g. affine structure-from-motion, photometric stereo, and non-rigid structure from motion. We specifically target structured data patterns, with outliers and large amounts of missing data. Using recently developed characterizations of minimal solutions to matrix factorization problems with missing data, we show how these can be used as building blocks in a hierarchical system that performs bootstrapping on all levels. This gives an robust and fast system, with state-of-the-art performance.}},
author = {{Oskarsson, Magnus and Batstone, Kenneth and Åström, Kalle}},
booktitle = {{2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), proceedings of}},
language = {{eng}},
month = {{06}},
pages = {{5820--5829}},
publisher = {{Computer Vision Foundation}},
title = {{Trust No One: Low Rank Matrix Factorization Using Hierarchical RANSAC}},
url = {{http://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Oskarsson_Trust_No_One_CVPR_2016_paper.html}},
year = {{2016}},
}