| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0492500 |
| utime |
20240103220354.0 |
| mtime |
20180827235959.9 |
| SCOPUS |
85059741571 |
| WOS |
000455146801028 |
| DOI |
10.1109/ICPR.2018.8545322 |
| title
(primary) (eng) |
BTF Compound Texture Model with Non-Parametric Control Field |
| specification |
| page_count |
6 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0492502 |
| ISBN |
978-1-5386-3787-6 |
| title
|
The 24th International Conference on Pattern Recognition (ICPR 2018) |
| page_num |
1151-1156 |
| publisher |
| place |
New York |
| name |
IEEE |
| year |
2018 |
|
|
| keyword |
Compound Markov random field model |
| keyword |
Bidirectional texture function |
| 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 |
| abstract
(eng) |
This paper introduces a novel multidimensional statistical model for realistic modeling, enlargement, editing, and compression of the recent state-of-the-art bidirectional texture function (BTF) textural representation. The presented multispectral compound Markov random field model (CMRF) efficiently fuses a non-parametric random field model with several parametric random fields models. The primary purpose of our modeling texture approach is to reproduce, compress, and enlarge a given measured natural or artificial texture image so that ideally both natural and synthetic texture will be visually indiscernible for any observation or illumination directions. However, the model can be easily applied for BFT material texture editing as well. The CMRF model consists of several parametric sub-models each having different characteristics along with an underlying switching structure model which controls transitions between these submodels. The proposed model uses the non-parametric random field for distributing local texture models in the form of analytically solvable wide-sense BTF Markov representation for single regions among the fields of a mosaic approximated by the random field structure model. The non-parametric control field of BTF-CMRF is reiteratively generated to guarantee identical region-size histograms for all material sub-classes present in the target example texture. The local texture regions (not necessarily continuous) are represented by analytical BTF models modeled by the adaptive 3D causal auto-regressive (3DCAR) random field model which can be analytically estimated as well as synthesized. The visual quality of the resulting complex synthetic textures generally surpasses the outputs of the previously published simpler non-compound BTF-MRF models. The model allows reaching huge compression ratio incomparable with any standard image compression method.\n |
| action |
| ARLID |
cav_un_auth*0363310 |
| name |
The 24th International Conference on Pattern Recognition (ICPR 2018) |
| dates |
20180820 |
| mrcbC20-s |
20180824 |
| place |
Beijing |
| country |
CN |
|
| RIV |
BD |
| FORD0 |
20000 |
| FORD1 |
20200 |
| FORD2 |
20205 |
| reportyear |
2019 |
| num_of_auth |
2 |
| mrcbC52 |
4 A hod 4ah 20231122143343.1 |
| presentation_type |
PR |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0286556 |
| mrcbC64 |
1 Department of Pattern Recognition UTIA-B 10201 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE |
| confidential |
S |
| mrcbC86 |
n.a. Proceedings Paper Computer Science Artificial Intelligence |
| arlyear |
2018 |
| mrcbTft |
\nSoubory v repozitáři: haindl-0492500.pdf |
| mrcbU14 |
85059741571 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
000455146801028 WOS |
| mrcbU63 |
cav_un_epca*0492502 The 24th International Conference on Pattern Recognition (ICPR 2018) 978-1-5386-3787-6 1151 1156 New York IEEE 2018 |
|