| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0467541 |
| utime |
20240103213205.2 |
| mtime |
20161219235959.9 |
| SCOPUS |
85019076115 |
| WOS |
000406771302004 |
| DOI |
10.1109/ICPR.2016.7899934 |
| title
(primary) (eng) |
Three-dimensional Gaussian Mixture Texture Model |
| specification |
| page_count |
6 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0467540 |
| ISBN |
978-1-5090-4846-5 |
| title
|
Proceedings of the 23rd International Conference on Pattern Recognition (ICPR) |
| page_num |
2026-2031 |
| publisher |
| place |
Piscataway |
| name |
IEEE |
| year |
2016 |
|
|
| keyword |
bidirectional texture function |
| keyword |
Gaussian mixture model |
| keyword |
texture modeling |
| 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 |
| ARLID |
cav_un_auth*0303439 |
| project_id |
GA14-10911S |
| agency |
GA ČR |
| country |
CZ |
|
| abstract
(eng) |
Visual texture modeling based on multidimensional mathematical models is the prerequisite for both robust material recognition as well as for image restoration, compression or numerous physically correct virtual reality applications. A novel multispectral visual texture modeling method based on a descriptive, unusually complex, three-dimensional, spatial Gaussian mixture model is presented. Texture synthesis benefits from easy computation of arbitrary conditional distributions from the model. The model is inherently multispectral thus it does not suffer with the spectral quality compromises of the spectrally factorized alternative approaches. The model is especially well suited for multispectral textile textures and it can also describe the most advanced textural representation in the form of a bidirectional texture function (BTF). |
| action |
| ARLID |
cav_un_auth*0340495 |
| name |
23rd International Conference on Pattern Recognition ICPR 2016 |
| dates |
20161204 |
| mrcbC20-s |
20161208 |
| place |
Cancún |
| country |
MX |
|
| RIV |
BD |
| reportyear |
2017 |
| num_of_auth |
2 |
| presentation_type |
PO |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0266451 |
| confidential |
S |
| article_num |
1003 |
| mrcbC86 |
3+4 Proceedings Paper Computer Science Artificial Intelligence |
| arlyear |
2016 |
| mrcbU14 |
85019076115 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
000406771302004 WOS |
| mrcbU63 |
cav_un_epca*0467540 Proceedings of the 23rd International Conference on Pattern Recognition (ICPR) 978-1-5090-4846-5 2026 2031 Piscataway IEEE 2016 |
|