Accurate Closed-form Estimation of Local Affine Transformations Consistent with the Epipolar Geometry
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F16%3A00306052" target="_blank" >RIV/68407700:21230/16:00306052 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Accurate Closed-form Estimation of Local Affine Transformations Consistent with the Epipolar Geometry
Original language description
For a pair of images satisfying the epipolar constraint, a method for accurate estimation of local affine transformations is proposed. The method returns the local affine transformation consistent with the epipolar geometry that is closest in the least squares sense to the initial estimate provided by an affine-covariant detector. The minimized L2-norm of the affine matrix elements is found in closed-form. We show that the used norm has an intuitive geometric interpretation. The method, with negligible computational requirements, is validated on publicly available benchmarking datasets and on synthetic data. The accuracy of the local affine transformations is improved for all detectors and all image pairs. Implicitly, precision of the tested feature detectors was compared. The Hessian-Affine detector combined with ASIFT view synthesis was the most accurate.
Czech name
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Czech description
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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
<a href="/en/project/GBP103%2F12%2FG084" target="_blank" >GBP103/12/G084: Center for Large Scale Multi-modal Data Interpretation</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2016
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 the British Machine Vision Conference (BMVC) 2016
ISBN
1-901725-53-7
ISSN
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e-ISSN
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Number of pages
12
Pages from-to
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Publisher name
British Machine Vision Association
Place of publication
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Event location
York
Event date
Sep 19, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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