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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Abstract	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 and comparing 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 hypothesize-and-verify structure of standard RANSAC to incorporate a number of important practical and computational considerations. In addition, we provide a general-purpose C++ software library that implements the USAC framework by leveraging state-of-the-art algorithms for the various modules. This implementation thus addresses many of the limitations of standard RANSAC within a single unified package. We benchmark the performance of the algorithm on a large collection of estimation problems. The implementation we provide can be used by researchers either as a stand-alone tool for robust estimation or as a benchmark for evaluating new techniques.
AbstractList	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 and comparing 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 hypothesize-and-verify structure of standard RANSAC to incorporate a number of important practical and computational considerations. In addition, we provide a general-purpose C++ software library that implements the USAC framework by leveraging state-of-the-art algorithms for the various modules. This implementation thus addresses many of the limitations of standard RANSAC within a single unified package. We benchmark the performance of the algorithm on a large collection of estimation problems. The implementation we provide can be used by researchers either as a stand-alone tool for robust estimation or as a benchmark for evaluating new techniques.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 and comparing 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 hypothesize-and-verify structure of standard RANSAC to incorporate a number of important practical and computational considerations. In addition, we provide a general-purpose C++ software library that implements the USAC framework by leveraging state-of-the-art algorithms for the various modules. This implementation thus addresses many of the limitations of standard RANSAC within a single unified package. We benchmark the performance of the algorithm on a large collection of estimation problems. The implementation we provide can be used by researchers either as a stand-alone tool for robust estimation or as a benchmark for evaluating new techniques. 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 and comparing 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 hypothesize-and-verify structure of standard RANSAC to incorporate a number of important practical and computational considerations. In addition, we provide a general-purpose C++ software library that implements the USAC framework by leveraging state-of-the-art algorithms for the various modules. This implementation thus addresses many of the limitations of standard RANSAC within a single unified package. We benchmark the performance of the algorithm on a large collection of estimation problems. The implementation we provide can be used by researchers either as a stand-alone tool for robust estimation or as a benchmark for evaluating new techniques.
Author	Raguram, R. Chum, O. Matas, J. Pollefeys, M. Frahm, J.
Author_xml	– sequence: 1 givenname: R. surname: Raguram fullname: Raguram, R. email: rraguram@apple.com organization: Apple, Inc., Cupertino, CA, USA – sequence: 2 givenname: O. surname: Chum fullname: Chum, O. email: chum@cmp.felk.cvut.cz organization: Czech Tech. Univ., Prague, Czech Republic – sequence: 3 givenname: M. surname: Pollefeys fullname: Pollefeys, M. email: marc.pollefeys@inf.ethz.ch organization: Dept. of Comput. Sci., ETH Zurich, Zurich, Switzerland – sequence: 4 givenname: J. surname: Matas fullname: Matas, J. email: matas@cmp.felk.cvut.cz organization: Czech Tech. Univ., Prague, Czech Republic – sequence: 5 givenname: J. surname: Frahm fullname: Frahm, J. email: jmf@cs.unc.edu organization: Dept. of Comput. Sci., Univ. of North Carolina at Chapel Hill, Chapel Hill, NC, USA
BackLink	http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=27677607$$DView record in Pascal Francis https://www.ncbi.nlm.nih.gov/pubmed/23787350$$D View this record in MEDLINE/PubMed
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Snippet	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...
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SubjectTerms	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
Title	USAC: A Universal Framework for Random Sample Consensus
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