| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0602524 |
| utime |
20241213113904.5 |
| mtime |
20241209235959.9 |
| DOI |
10.1007/978-3-031-78172-8_21 |
| title
(primary) (eng) |
Texture Spectral Decorrelation Criteria |
| specification |
| page_count |
10 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0602523 |
| ISBN |
978-3-031-78172-8 |
| ISSN |
0302-9743 |
| title
|
Pattern Recognition : 27th International Conference, ICPR 2024 |
| part_title |
Lecture Notes in Computer Science |
| page_num |
324-333 |
| publisher |
| place |
Cham |
| name |
Springer Nature Switzerland |
| year |
2025 |
|
| editor |
| name1 |
Antonacopoulos |
| name2 |
Apostolos |
|
| editor |
| name1 |
Chaudhuri |
| name2 |
Subhasis |
|
| editor |
| name1 |
Chellappa |
| name2 |
Rama |
|
| editor |
| name1 |
Liu |
| name2 |
Cheng-Lin |
|
| editor |
| name1 |
Bhattacharya |
| name2 |
Saumik |
|
| editor |
|
|
| keyword |
Texture spectral quality comparison |
| keyword |
Texture modeling |
| keyword |
Texture synthesis |
| keyword |
Bidirectional texture function |
| author
(primary) |
| ARLID |
cav_un_auth*0101093 |
| name1 |
Haindl |
| name2 |
Michal |
| institution |
UTIA-B |
| full_dept (cz) |
Rozpoznávání obrazu |
| full_dept (eng) |
Department of Pattern Recognition |
| department (cz) |
RO |
| department (eng) |
RO |
| 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 |
| institution |
UTIA-B |
| department |
RO |
| full_dept (cz) |
Rozpoznávání obrazu |
| full_dept |
Department of Pattern Recognition |
| department (cz) |
RO |
| department |
RO |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| abstract
(eng) |
We introduce texture spectral criteria, which allow us to predict whether simplified spectrally factorized random field-based texture models, a set of two-dimensional models, can faithfully replicate texture spectra compared to their fully spectrally correlated 3D counterparts. These probabilistic models incorporate essential two- or three-dimensional building factors for modeling the seven-dimensional Bidirectional Texture Function (BTF), the most advanced representation in real-world material visual properties modeling. While these models seamlessly approximate original measured massive data and extend them to arbitrary sizes or simulate unmeasured textures, evaluating typically involves time-consuming synthesis and psycho-physical evaluation. The proposed criteria provide an alternative approach, enabling us to bypass the spectral quality evaluation step. |
| action |
| ARLID |
cav_un_auth*0478337 |
| name |
International Conference on Pattern Recognition 2024 /27./ |
| dates |
20241201 |
| mrcbC20-s |
20241205 |
| place |
Kolkata |
| country |
IN |
|
| RIV |
BD |
| FORD0 |
20000 |
| FORD1 |
20200 |
| FORD2 |
20202 |
| reportyear |
2025 |
| num_of_auth |
2 |
| presentation_type |
PO |
| inst_support |
RVO:67985556 |
| permalink |
https://hdl.handle.net/11104/0360012 |
| confidential |
S |
| article_num |
21 |
| arlyear |
2025 |
| mrcbU14 |
SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
WOS |
| mrcbU63 |
cav_un_epca*0602523 Pattern Recognition : 27th International Conference, ICPR 2024 Springer Nature Switzerland 2025 Cham 324 333 978-3-031-78172-8 Lecture Notes in Computer Science 15306 0302-9743 1611-3349 |
| mrcbU67 |
Antonacopoulos Apostolos 340 |
| mrcbU67 |
Chaudhuri Subhasis 340 |
| mrcbU67 |
Chellappa Rama 340 |
| mrcbU67 |
Liu Cheng-Lin 340 |
| mrcbU67 |
Bhattacharya Saumik 340 |
| mrcbU67 |
Pal Umapada 340 |
|