Randomized RANSAC with Sequential Probability Ratio Test
Result description
A randomized model verification strategy for RANSAC is presented. The proposed method finds, like RANSAC, a solution that is optimal with user-controllable probability. A provably optimal model verification strategy is designed for the situation when thecontamination of data by outliers is known, i.e. the algorithm is the fastest possible (on average) of all randomized RANSAC algorithms guaranteeing confidence in the solution. The derivation of the optimality property is based on Wald.s theory of sequential decision making. The R-RANSAC with SPRT, which does not require the a priori knowledge of the fraction of outliers and has results close to the optimal strategy, is introduced. We show experimentally that on standard test data the method is 2 to 10times faster than the standard RANSAC and up to 4 times faster than previously published methods.
Keywords
RANSACSPRTepipolar geometryhomographyrandomised verificationrobust estimation
The result's identifiers
Result code in IS VaVaI
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Randomized RANSAC with Sequential Probability Ratio Test
Original language description
A randomized model verification strategy for RANSAC is presented. The proposed method finds, like RANSAC, a solution that is optimal with user-controllable probability. A provably optimal model verification strategy is designed for the situation when thecontamination of data by outliers is known, i.e. the algorithm is the fastest possible (on average) of all randomized RANSAC algorithms guaranteeing confidence in the solution. The derivation of the optimality property is based on Wald.s theory of sequential decision making. The R-RANSAC with SPRT, which does not require the a priori knowledge of the fraction of outliers and has results close to the optimal strategy, is introduced. We show experimentally that on standard test data the method is 2 to 10times faster than the standard RANSAC and up to 4 times faster than previously published methods.
Czech name
Není k dispozici
Czech description
Není k dispozici
Classification
Type
D - Article in proceedings
CEP classification
JD - Use of computers, robotics and its application
OECD FORD branch
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Result continuities
Project
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2005
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of IEEE International Conference on Computer Vision (ICCV)
ISBN
0-7695-2334-X
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
1727-1732
Publisher name
IEEE Computer Society Press
Place of publication
New York
Event location
Bejing
Event date
Oct 15, 2005
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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Basic information
Result type
D - Article in proceedings
CEP
JD - Use of computers, robotics and its application
Year of implementation
2005