| bibtype |
M -
Monography Chapter
|
| ARLID |
0343263 |
| utime |
20240111140739.9 |
| mtime |
20100617235959.9 |
| title
(primary) (eng) |
Illumination Invariants Based on Markov Random Fields |
| specification |
| page_count |
20 s. |
| media_type |
www |
| book_pages |
524 |
|
| serial |
| ARLID |
cav_un_epca*0342819 |
| ISBN |
978-953-7619-90-9 |
| title
|
Pattern Recognition, Recent Advances |
| page_num |
253-272 |
| publisher |
| place |
Vukovar, Croatia |
| name |
In-Teh |
| year |
2010 |
|
| editor |
|
|
| keyword |
illumination invariants |
| keyword |
textural features |
| keyword |
Markov random fields |
| 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 |
2C06019 |
| agency |
GA MŠk |
| country |
CZ |
| ARLID |
cav_un_auth*0216518 |
|
| project |
| project_id |
GA102/08/0593 |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0239567 |
|
| research |
CEZ:AV0Z10750506 |
| abstract
(eng) |
Content-based image retrieval systems (CBIR) typically query large image databases based on some automatically generated colour and textural features. Optimal robust features should be geometry and illumination invariant. Although image retrieval has been an active research area for many years this difficult problem is still far from being solved. We introduce fast and robust textural features that allow retrieving images with similar scenes comprising colour textured objects viewed with different illumination. The proposed textural features that are invariant to illumination spectrum and extremely robust to illumination direction. They require only a single training image per texture and no knowledge of illumination direction, brightness or spectrum. These feature utilises utilise illumination invariant features extracted from three different Markov random field (MRF) based texture representations. |
| reportyear |
2011 |
| RIV |
BD |
| permalink |
http://hdl.handle.net/11104/0185780 |
| arlyear |
2010 |
| mrcbU56 |
pdf |
| mrcbU63 |
cav_un_epca*0342819 Pattern Recognition, Recent Advances 978-953-7619-90-9 253 272 Vukovar, Croatia In-Teh 2010 |
| mrcbU67 |
Herout A. 340 |
|