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Texture Segmentation Benchmark

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F22%3A00545221" target="_blank" >RIV/67985556:_____/22:00545221 - isvavai.cz</a>

  • Result on the web

    <a href="https://ieeexplore.ieee.org/document/9416785" target="_blank" >https://ieeexplore.ieee.org/document/9416785</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/TPAMI.2021.3075916" target="_blank" >10.1109/TPAMI.2021.3075916</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Texture Segmentation Benchmark

  • Original language description

    The Prague texture segmentation data-generator and benchmark (href{https://mosaic.utia.cas.cz}{mosaic.utia.cas.cz}) is a web-based service designed to mutually compare and rank (recently nearly 200) different static and dynamic texture and image segmenters, to find optimal parametrization of a segmenter and support the development of new segmentation and classification methods. The benchmark verifies segmenter performance characteristics on potentially unlimited monospectral, multispectral, satellite, and bidirectional texture function (BTF) data using an extensive set of over forty prevalent criteria. It also enables us to test for noise robustness and scale, rotation, or illumination invariance. It can be used in other applications, such as feature selection, image compression, query by pictorial example, etc. The benchmark's functionalities are demonstrated in evaluating several examples of leading previously published unsupervised and supervised image segmentation algorithms. However, they are used to illustrate the benchmark functionality and not review the recent image segmentation state-of-the-art.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    20204 - Robotics and automatic control

Result continuities

  • Project

    <a href="/en/project/GA19-12340S" target="_blank" >GA19-12340S: Surface material recognition under variable optical observation conditions</a><br>

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2022

  • 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

    1939-3539

  • Volume of the periodical

    44

  • Issue of the periodical within the volume

    9

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    16

  • Pages from-to

    5647-5663

  • UT code for WoS article

    000836666600081

  • EID of the result in the Scopus database

    2-s2.0-85105053349