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...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	IEEE transactions on pattern analysis and machine intelligence Ročník 35; číslo 8; s. 2022 - 2038
Hlavní autoři:	Raguram, R., Chum, O., Pollefeys, M., Matas, J., Frahm, J.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Los Alamitos, CA IEEE 01.08.2013 IEEE Computer Society
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!