0380288 - ÚTIA 2013 RIV DE eng C - Konferenční příspěvek (zahraniční konf.)
Vácha, Pavel - Haindl, Michal
Texture Recognition using Robust Markovian Features.
Computational Intelligence for Multimedia Understanding. Berlin: Springer, 2012, s. 126-137. Lecture Notes in Computer Science, 7252. ISBN 978-3-642-32435-2. ISSN 0302-9743.
[MUSCLE. Pisa (IT), 13.12.2011-15.12.2011]
Grant CEP: GA MŠMT 1M0572; GA ČR GAP103/11/0335; GA ČR GA102/08/0593
Grant ostatní: CESNET(CZ) 387/2010
Institucionální podpora: RVO:67985556
Klíčová slova: texture recognition * illumination invariance * Markov random fields * Bidirectional Texture Function * textural databases
Kód oboru RIV: BD - Teorie informace
Web výsledku:
http://library.utia.cas.cz/separaty/2012/RO/vacha-texture recognition using robust markovian features.pdf

DOI: https://doi.org/10.1007/978-3-642-32436-9_11

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).
Trvalý link: http://hdl.handle.net/11104/0211030