| bibtype |
J -
Journal Article
|
| ARLID |
0545221 |
| utime |
20250310160047.4 |
| mtime |
20210906235959.9 |
| SCOPUS |
85105053349 |
| WOS |
000836666600081 |
| DOI |
10.1109/TPAMI.2021.3075916 |
| title
(primary) (eng) |
Texture Segmentation Benchmark |
| specification |
| page_count |
16 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0256725 |
| ISSN |
0162-8828 |
| title
|
IEEE Transactions on Pattern Analysis and Machine Intelligence |
| volume_id |
44 |
| volume |
9 (2022) |
| page_num |
5647-5663 |
| publisher |
| name |
IEEE Computer Society |
|
|
| keyword |
Benchmark |
| keyword |
Image segmentation |
| keyword |
Texture segmentation |
| keyword |
(Un)supervised segmentation |
| keyword |
Segmentation criteria |
| keyword |
Scale, rotation and illumination invariants |
| author
(primary) |
| ARLID |
cav_un_auth*0101165 |
| name1 |
Mikeš |
| name2 |
Stanislav |
| institution |
UTIA-B |
| full_dept (cz) |
Rozpoznávání obrazu |
| full_dept (eng) |
Department of Pattern Recognition |
| department (cz) |
RO |
| department (eng) |
RO |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| 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 |
| project |
| project_id |
GA19-12340S |
| agency |
GA ČR |
| country |
CZ |
| ARLID |
cav_un_auth*0376011 |
|
| abstract
(eng) |
The Prague texture segmentation data-generator and benchmark (\href{https://mosaic.utia.cas.cz}{mosaic.utia.cas.cz}) is a web-based service designed to mutually compare and rank (recently nearly 200) different static and dynamic texture and image segmenters, to find optimal parametrization of a segmenter and support the development of new segmentation and classification methods. The benchmark verifies segmenter performance characteristics on potentially unlimited monospectral, multispectral, satellite, and bidirectional texture function (BTF) data using an extensive set of over forty prevalent criteria. It also enables us to test for noise robustness and scale, rotation, or illumination invariance. It can be used in other applications, such as feature selection, image compression, query by pictorial example, etc. The benchmark's functionalities are demonstrated in evaluating several examples of leading previously published unsupervised and supervised image segmentation algorithms. However, they are used to illustrate the benchmark functionality and not review the recent image segmentation state-of-the-art. |
| result_subspec |
WOS |
| RIV |
BD |
| FORD0 |
20000 |
| FORD1 |
20200 |
| FORD2 |
20204 |
| reportyear |
2023 |
| num_of_auth |
2 |
| mrcbC52 |
4 A sml 4as 2rh 20231122145922.3 2 R hod 20250310153941.7 20250310160047.4 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0322145 |
| confidential |
S |
| contract |
| name |
IEEE COPYRIGHT FORM |
| date |
20210421 |
|
| mrcbC86 |
1 Article Computer Science Artificial Intelligence|Engineering Electrical Electronic |
| mrcbC91 |
C |
| mrcbT16-e |
COMPUTERSCIENCEARTIFICIALINTELLIGENCE|ENGINEERINGELECTRICALELECTRONIC |
| mrcbT16-j |
7.008 |
| mrcbT16-s |
4.447 |
| mrcbT16-D |
Q1* |
| mrcbT16-E |
Q1* |
| arlyear |
2022 |
| mrcbTft |
\nSoubory v repozitáři: haindl-0545221.pdf, haindl-0545221-CopyrightReceipt.pdf |
| mrcbU14 |
85105053349 SCOPUS |
| mrcbU24 |
33905324 PUBMED |
| mrcbU34 |
000836666600081 WOS |
| mrcbU63 |
cav_un_epca*0256725 IEEE Transactions on Pattern Analysis and Machine Intelligence 0162-8828 1939-3539 Roč. 44 č. 9 2022 5647 5663 IEEE Computer Society |
|