USAC: A Universal Framework for Random Sample Consensus
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F13%3A00212571" target="_blank" >RIV/68407700:21230/13:00212571 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/TPAMI.2012.257" target="_blank" >http://dx.doi.org/10.1109/TPAMI.2012.257</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TPAMI.2012.257" target="_blank" >10.1109/TPAMI.2012.257</a>
Alternative languages
Result language
angličtina
Original language name
USAC: A Universal Framework for Random Sample Consensus
Original language description
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 some means of dealing with data contamination. The random sample consensus (RANSAC) algorithm is one of the most popular tools for robust estimation. Recent years have seen an explosion of activity in this area,leading to the development of a number of techniques that improve upon the efficiency and robustness of the basic RANSAC algorithm. In this paper, we present a comprehensive overview of recent research in RANSAC-based robust estimation by analyzing andcomparing various approaches that have been explored over the years. We provide a common context for this analysis by introducing a new framework for robust estimation, which we call Universal RANSAC (USAC). USAC extends the simple hypoth
Czech name
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Czech description
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Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
JD - Use of computers, robotics and its application
OECD FORD branch
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Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Transactions on Pattern Analysis and Machine Intelligence
ISSN
0162-8828
e-ISSN
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Volume of the periodical
35
Issue of the periodical within the volume
8
Country of publishing house
US - UNITED STATES
Number of pages
17
Pages from-to
2022-2038
UT code for WoS article
000320381400016
EID of the result in the Scopus database
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