USAC: A Universal Framework for Random Sample Consensus

A computational problem that arises frequently in computer vision is that of estimating the parameters of a model from data that have been contaminated by noise and outliers. More generally, any practical system that seeks to estimate quantities from noisy data measurements must have at its core som...

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Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence Jg. 35; H. 8; S. 2022 - 2038
Hauptverfasser:	Raguram, R., Chum, O., Pollefeys, M., Matas, J., Frahm, J.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Los Alamitos, CA IEEE 01.08.2013 IEEE Computer Society
Schlagworte:	Algorithm design and analysis Algorithmics. Computability. Computer arithmetics Applied sciences Artificial intelligence Computational modeling Computer science; control theory; systems Context Data models Estimation Exact sciences and technology Pattern recognition. Digital image processing. Computational geometry RANSAC robust estimation Robustness Software Software engineering Theoretical computing Computer vision Parameter estimation Program library Estimator robustness Activity C language Contamination Standards Outlier RANSAC Software libraries Model matching Efficiency robust estimation Data models Signal to noise ratio
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
Online-Zugang:	Volltext
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