| 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 |
|
| 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 |
|
| 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 |
|