| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0380289 |
| utime |
20240103201154.4 |
| mtime |
20120921235959.9 |
| DOI |
10.1007/978-3-642-32436-9_13 |
| title
(primary) (eng) |
Bidirectional Texture Function Simultaneous Autoregressive Model |
| specification |
| page_count |
11 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 |
149-159 |
| publisher |
| place |
Berlin |
| name |
Springer |
| year |
2012 |
|
|
| keyword |
bidirectional texture function |
| keyword |
texture analysis |
| keyword |
texture synthesis |
| keyword |
data compression |
| keyword |
virtual reality |
| author
(primary) |
| ARLID |
cav_un_auth*0101093 |
| name1 |
Haindl |
| name2 |
Michal |
| 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*0283206 |
| name1 |
Havlíček |
| 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 |
GA102/08/0593 |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0239567 |
|
| project |
| project_id |
GAP103/11/0335 |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0273627 |
|
| abstract
(eng) |
The Bidirectional Texture Function (BTF) is the recent most advanced representation of visual properties of surface materials. It specifies their altering appearance due to varying illumination and viewing conditions. Corresponding huge BTF measurements require a mathematical representation allowing simultaneously extremal compression as well as high visual fidelity. We present a novel Markovian BTF model based on a set of underlying simultaneous autoregressive models (SAR). This complex but efficient BTF-SAR model combines several multispectral band limited spatial factors and range map sub-models to produce the required BTF texture space. The BTF-SAR model enables very high BTF space compression ratio, texture enlargement, and reconstruction of missing unmeasured parts of the BTF space. |
| 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/11104/0211031 |
| 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 149 159 Berlin Springer 2012 Lecture Notes in Computer Science 7252 |
|