| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0380283 |
| utime |
20240103201154.0 |
| mtime |
20120921235959.9 |
| DOI |
10.1007/978-3-642-32436-9_12 |
| title
(primary) (eng) |
A Plausible Texture Enlargement and Editing Compound Markovian 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 |
138-148 |
| publisher |
| place |
Berlin |
| name |
Springer |
| year |
2012 |
|
|
| keyword |
compound Markov random field |
| keyword |
bidirectional texture function |
| keyword |
texture editing |
| 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*0101100 |
| name1 |
Havlíček |
| name2 |
Vojtěch |
| 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 |
GA102/08/0593 |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0239567 |
|
| project |
| project_id |
1M0572 |
| agency |
GA MŠk |
| ARLID |
cav_un_auth*0001814 |
|
| project |
| project_id |
GAP103/11/0335 |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0273627 |
|
| project |
| project_id |
387/2010 |
| agency |
CESNET |
| country |
CZ |
|
| abstract
(eng) |
This paper describes high visual quality compound Markov random field texture model capable to realistically model multispectral bidirectional texture function, which is currently the most advanced representation of visual properties of surface materials. The presented compound Markov random field model combines a non-parametric control random field with analytically solvable wide-sense Markov representation for single regions and thus allows very efficient non-iterative parameters estimation as well as the compound random field synthesis. The compound Markov random field model is utilized for realistic texture compression, enlargement, and powerful automatic texture editing. Edited textures maintain their original layout but adopt anticipated local characteristics from one or several parent target textures. |
| 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 |
| permalink |
http://hdl.handle.net/11104/0211026 |
| 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 138 148 Berlin Springer 2012 Lecture Notes in Computer Science 7252 |
|