| bibtype |
J -
Journal Article
|
| ARLID |
0506602 |
| utime |
20240103222303.5 |
| mtime |
20190719235959.9 |
| SCOPUS |
85068558335 |
| WOS |
000482374500084 |
| DOI |
10.1016/j.patrec.2019.06.027 |
| title
(primary) (eng) |
Bark recognition using novel rotationally invariant multispectral textural features |
| specification |
| page_count |
6 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0257389 |
| ISSN |
0167-8655 |
| title
|
Pattern Recognition Letters |
| volume_id |
125 |
| volume |
1 (2019) |
| page_num |
612-617 |
| publisher |
|
|
| keyword |
Bark recognition |
| keyword |
Tree taxonomy clasification |
| keyword |
Spiral Markov random field model |
| keyword |
textural feature |
| author
(primary) |
| ARLID |
cav_un_auth*0286710 |
| name1 |
Remeš |
| name2 |
Václav |
| 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*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 |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| source |
|
| cas_special |
| project |
| ARLID |
cav_un_auth*0376011 |
| project_id |
GA19-12340S |
| agency |
GA ČR |
| country |
CZ |
|
| abstract
(eng) |
We present novel rotationally invariant fully multispectral Markovian textural features applied for the efficient tree bark recognition. These textural features are derived from the novel descriptive multispectral spiral wide-sense Markov model. Unlike the alternative bark recognition methods based on various gray-scale discriminative textural descriptions, we benefit from fully descriptive color, rotationally invariant bark texture representation. The proposed methods significantly outperform the state-of-the-art bark recognition approaches regarding classification accuracy. Both our classifiers outperform convolutional neural network ResNet even on the largest public bark database BarkNet which contains 23 000 high-resolution images from 23 different tree species. |
| result_subspec |
WOS |
| RIV |
BD |
| FORD0 |
20000 |
| FORD1 |
20200 |
| FORD2 |
20202 |
| reportyear |
2020 |
| num_of_auth |
2 |
| mrcbC52 |
4 A hod 4ah 20231122144131.0 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0297826 |
| mrcbC64 |
1 Department of Pattern Recognition UTIA-B 10201 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE |
| confidential |
S |
| mrcbC86 |
2 Article Computer Science Artificial Intelligence |
| mrcbC91 |
C |
| mrcbT16-e |
COMPUTERSCIENCEARTIFICIALINTELLIGENCE |
| mrcbT16-j |
0.844 |
| mrcbT16-s |
0.848 |
| mrcbT16-B |
62.394 |
| mrcbT16-D |
Q2 |
| mrcbT16-E |
Q4 |
| arlyear |
2019 |
| mrcbTft |
\nSoubory v repozitáři: haindl-0506602.pdf |
| mrcbU14 |
85068558335 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
000482374500084 WOS |
| mrcbU63 |
cav_un_epca*0257389 Pattern Recognition Letters 0167-8655 1872-7344 Roč. 125 č. 1 2019 612 617 Elsevier |
|