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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

  • DOI - Digital Object Identifier

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

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    JD - Use of computers, robotics and its application

  • OECD FORD branch

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

  • e-ISSN

  • Number of pages

    12

  • Pages from-to

  • Publisher name

    British Machine Vision Association

  • Place of publication

  • Event location

    York

  • Event date

    Sep 19, 2016

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article