Texture Recognition using Robust Markovian Features
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F12%3A00380288" target="_blank" >RIV/67985556:_____/12:00380288 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-642-32436-9_11" target="_blank" >http://dx.doi.org/10.1007/978-3-642-32436-9_11</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-642-32436-9_11" target="_blank" >10.1007/978-3-642-32436-9_11</a>
Alternative languages
Result language
angličtina
Original language name
Texture Recognition using Robust Markovian Features
Original language description
We provide a thorough experimental evaluation of several state-of-the-art textural features on four representative and extensive image data/-bases. Each of the experimental textural databases ALOT, Bonn BTF, UEA Uncalibrated, and KTH-TIPS2 aims at specific part of realistic acquisition conditions of surface materials represented as multispectral textures. The extensive experimental evaluation proves the outstanding reliable and robust performance of efficient Markovian textural features analytically derived from a wide-sense Markov random field causal model. These features systematically outperform leading Gabor, Opponent Gabor, LBP, and LBP-HF alternatives. Moreover, they even allow successful recognition of arbitrary illuminated samples using a single training image per material. Our features are successfully applied also for the recent most advanced textural representation in the form of 7-dimensional Bidirectional Texture Function (BTF).
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
BD - Information theory
OECD FORD branch
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Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Computational Intelligence for Multimedia Understanding
ISBN
978-3-642-32435-2
ISSN
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e-ISSN
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Number of pages
12
Pages from-to
126-137
Publisher name
Springer
Place of publication
Berlin
Event location
Pisa
Event date
Dec 13, 2011
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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