Trust No One: Low Rank Matrix Factorization Using Hierarchical RANSAC
Oskarsson, Magnus; Batstone, Kenneth; Åström, Kalle (2016-06-01). Trust No One: Low Rank Matrix Factorization Using Hierarchical RANSAC 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), proceedings of, 5820 - 5829. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Seattle, United States: Computer Vision Foundation
Conference Proceeding/Paper
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Published
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English
Authors:
Oskarsson, Magnus
;
Batstone, Kenneth
;
Åström, Kalle
Department:
Mathematics (Faculty of Engineering)
Mathematical Imaging Group
Centre for Mathematical Sciences
ELLIIT: the Linköping-Lund initiative on IT and mobile communication
eSSENCE: The e-Science Collaboration
Project:
Semantic Mapping and Visual Navigation for Smart Robots
Research Group:
Mathematical Imaging Group
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.
Keywords:
Mathematics ;
Computer Vision and Robotics (Autonomous Systems)
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