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