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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

  • Czech description

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

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

  • 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