bibtype C - Conference Paper (international conference)
ARLID 0346560
utime 20240103193759.0
mtime 20100907235959.9
DOI 10.1109/ICPR.2010.216
title (primary) (eng) Natural Material Recognition with Illumination Invariant Textural Features
specification
page_count 4 s.
serial
ARLID cav_un_epca*0346559
ISBN 978-1-4244-7542-1
ISSN 1051-4651
title 20th International Conference on Pattern Recognition
page_num 858-861
publisher
place Los Alamitos
name IEEE Computer Society CPS
year 2010
keyword texture
keyword colour
keyword Markov random field
keyword illumination invariance
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/2010/RO/vacha-natural material recognition with illumination invariant textural features.pdf
cas_special
project
project_id 1M0572
agency GA MŠk
ARLID cav_un_auth*0001814
project
project_id GA102/08/0593
agency GA ČR
ARLID cav_un_auth*0239567
project
project_id 2C06019
agency GA MŠk
country CZ
ARLID cav_un_auth*0216518
research CEZ:AV0Z10750506
abstract (eng) A visual appearance of natural materials fundamentally depends on illumination conditions, which significantly complicates a real scene analysis. We propose textural features based on fast Markovian statistics, which are simultaneously invariant to illumination colour and robust to illumination direction. No knowledge of illumination conditions is required and a recognition is possible from a single training image per material. Material recognition is tested on the currently most realistic visual representation - Bidirectional Texture Function (BTF), using the Amsterdam Library of Textures (ALOT), which contains 250 natural materials acquired in different illumination conditions. Our proposed features significantly outperform several leading alternatives including Local Binary Patterns (LBP, LBP-HF) and Gabor features.
action
ARLID cav_un_auth*0263643
name 20th International Conference on Pattern Recognition ICPR 2010
place Istanbul
dates 23.08.2010-26.08.2010
country TR
reportyear 2011
RIV BD
permalink http://hdl.handle.net/11104/0187560
mrcbT16-q 50
mrcbT16-s 0.420
mrcbT16-y 10.52
mrcbT16-x 0.85
arlyear 2010
mrcbU63 cav_un_epca*0346559 20th International Conference on Pattern Recognition 978-1-4244-7542-1 1051-4651 858 861 Los Alamitos IEEE Computer Society CPS 2010