bibtype C - Conference Paper (international conference)
ARLID 0380288
utime 20240103201154.3
mtime 20120921235959.9
DOI 10.1007/978-3-642-32436-9_11
title (primary) (eng) Texture Recognition using Robust Markovian Features
specification
page_count 12 s.
media_type P
serial
ARLID cav_un_epca*0380282
ISBN 978-3-642-32435-2
ISSN 0302-9743
title Computational Intelligence for Multimedia Understanding
page_num 126-137
publisher
place Berlin
name Springer
year 2012
keyword texture recognition
keyword illumination invariance
keyword Markov random fields
keyword Bidirectional Texture Function
keyword textural databases
author (primary)
ARLID cav_un_auth*0213290
name1 Vácha
name2 Pavel
full_dept (cz) Rozpoznávání obrazu
full_dept (eng) Department of Pattern Recognition
department (cz) RO
department (eng) RO
institution UTIA-B
full_dept Department of Pattern Recognition
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0101093
name1 Haindl
name2 Michal
full_dept (cz) Rozpoznávání obrazu
full_dept Department of Pattern Recognition
department (cz) RO
department RO
institution UTIA-B
full_dept Department of Pattern Recognition
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source
url http://library.utia.cas.cz/separaty/2012/RO/vacha-texture recognition using robust markovian features.pdf
cas_special
project
project_id 1M0572
agency GA MŠk
ARLID cav_un_auth*0001814
project
project_id 387/2010
agency CESNET
country CZ
project
project_id GAP103/11/0335
agency GA ČR
ARLID cav_un_auth*0273627
project
project_id GA102/08/0593
agency GA ČR
ARLID cav_un_auth*0239567
abstract (eng) 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).
action
ARLID cav_un_auth*0283201
name MUSCLE
place Pisa
dates 13.12.2011-15.12.2011
country IT
reportyear 2013
RIV BD
num_of_auth 2
presentation_type PR
inst_support RVO:67985556
permalink http://hdl.handle.net/11104/0211030
mrcbT16-q 100
mrcbT16-s 0.314
mrcbT16-y 16.66
mrcbT16-x 0.49
mrcbT16-4 Q2
mrcbT16-E Q2
arlyear 2012
mrcbU63 cav_un_epca*0380282 Computational Intelligence for Multimedia Understanding 978-3-642-32435-2 0302-9743 126 137 Berlin Springer 2012 Lecture Notes in Computer Science 7252