Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/108656
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Type: Conference paper
Title: High breakdown bundle adjustment
Author: Eriksson, A.
Isaksson, M.
Chin, T.
Citation: Proceedings of the 2015 IEEE Winter Conference on Applications of Computer Vision, 2015 / pp.310-317
Publisher: IEEE
Issue Date: 2015
ISBN: 9781479966820
Conference Name: 2015 IEEE Winter Conference on Applications of Computer Vision (WACV 2015) (05 Jan 2015 - 09 Jan 2015 : Waikoloa, HI)
Statement of
Responsibility: 
Anders Eriksson, Mats Isaksson, Tat-Jun Chin
Abstract: Identifying the parameters of a model such that it best fits an observed set of data points is fundamental to the majority of problems in computer vision. This task is particularly demanding when portions of the data has been corrupted by gross outliers, measurements that are not explained by the assumed distributions. In this paper we present a novel method that uses the Least Quantile of Squares (LQS) estimator, a well known but computationally demanding high-breakdown estimator with several appealing theoretical properties. The proposed method is a meta-algorithm, based on the well established principles of proximal splitting, that allows for the use of LQS estimators while still retaining computational efficiency. Implementing the method is straight-forward as the majority of the resulting sub-problems can be solved using existing standard bundle-adjustment packages. Preliminary experiments on synthetic and real image data demonstrate the impressive practical performance of our method as compared to existing robust estimators used in computer vision.
Keywords: Robustness, standards, computer vision, algorithm design and analysis, data models, educational institutions, computational modeling
Rights: © 2015 IEEE
RMID: 0030028593
DOI: 10.1109/WACV.2015.48
Grant ID: http://purl.org/au-research/grants/arc/DE130101775
Appears in Collections:Computer Science publications

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