| 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 |
|
| 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 |
|