| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0579556 |
| utime |
20240402214920.9 |
| mtime |
20231215235959.9 |
| SCOPUS |
85183540901 |
| DOI |
10.1016/j.procs.2023.10.308 |
| title
(primary) (eng) |
Multispectral Texture Benchmark |
| specification |
| page_count |
10 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0583149 |
| ISSN |
1877-0509 |
| title
|
Procedia Computer Science : Volume 225, 27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023 |
| page_num |
3143-3152 |
| publisher |
| place |
Amsterdam |
| name |
Elsevier |
| year |
2023 |
|
|
| keyword |
textural features |
| keyword |
benchmark |
| keyword |
representation |
| keyword |
multispectral features |
| author
(primary) |
| ARLID |
cav_un_auth*0021746 |
| name1 |
Kříž |
| name2 |
P. |
| country |
CZ |
|
| author
|
| ARLID |
cav_un_auth*0101093 |
| name1 |
Haindl |
| name2 |
Michal |
| institution |
UTIA-B |
| full_dept (cz) |
Rozpoznávání obrazu |
| full_dept |
Department of Pattern Recognition |
| department (cz) |
RO |
| department |
RO |
| full_dept |
Department of Pattern Recognition |
| garant |
A |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| source |
|
| cas_special |
| abstract
(eng) |
Dozens of textural features have been published, but their realistic validation for efficient recognition applications still needs to be discovered. Textural features are derived using various approaches. We present a benchmark that can be used to evaluate these features and categorize them based on their information efficiency. We propose how the features can be benchmarked and explain different ways of measuring their properties and performance. Most textural feature-extracting algorithms are only based on information extraction from monospectral images (gray-level). Apart from native multispectral algorithms, we generalize some of these originally monospectral features for hyperspectral textures in our illustrating examples. |
| action |
| ARLID |
cav_un_auth*0459925 |
| name |
International Conference on Knowledge-Based and Intelligent Information & Engineering Systems 2023 (KES 2023) /27./ |
| dates |
20230906 |
| mrcbC20-s |
20230908 |
| place |
Athens |
| country |
GR |
|
| RIV |
BD |
| FORD0 |
20000 |
| FORD1 |
20200 |
| FORD2 |
20205 |
| reportyear |
2024 |
| num_of_auth |
2 |
| presentation_type |
PR |
| inst_support |
RVO:67985556 |
| permalink |
https://hdl.handle.net/11104/0349560 |
| cooperation |
| ARLID |
cav_un_auth*0401576 |
| name |
ČVUT Fakulta informačních technologií |
| institution |
ČVUT FIT |
| country |
CZ |
|
| confidential |
S |
| mrcbC91 |
A |
| mrcbT16-s |
0.569 |
| mrcbT16-E |
Q3 |
| arlyear |
2023 |
| mrcbU02 |
C |
| mrcbU14 |
85183540901 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
WOS |
| mrcbU63 |
cav_un_epca*0583149 Procedia Computer Science : Volume 225, 27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023 Elsevier 2023 Amsterdam 3143 3152 1877-0509 |
|