| bibtype |
J -
Journal Article
|
| ARLID |
0345450 |
| utime |
20240103193650.7 |
| mtime |
20110816235959.9 |
| WOS |
000293961900002 |
| SCOPUS |
80053895602 |
| DOI |
10.1007/s00138-010-0251-6 |
| title
(primary) (eng) |
Dynamic texture as foreground and background |
| specification |
|
| serial |
| ARLID |
cav_un_epca*0254218 |
| ISSN |
0932-8092 |
| title
|
Machine Vision and Applications |
| volume_id |
22 |
| volume |
5 (2011) |
| page_num |
741-750 |
| publisher |
|
|
| keyword |
Dynamic texture |
| keyword |
Optical flow |
| keyword |
SVD |
| author
(primary) |
| ARLID |
cav_un_auth*0216372 |
| name1 |
Chetverikov |
| name2 |
D. |
| country |
HU |
|
| author
|
| ARLID |
cav_un_auth*0273706 |
| name1 |
Fazekas |
| name2 |
S. |
| country |
HU |
|
| author
|
| ARLID |
cav_un_auth*0101093 |
| name1 |
Haindl |
| name2 |
Michal |
| 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 |
| project |
| project_id |
GA102/08/0593 |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0239567 |
|
| project |
| project_id |
507752 |
| country |
XE |
| agency |
EC |
| ARLID |
cav_un_auth*0200689 |
|
| research |
CEZ:AV0Z10750506 |
| abstract
(eng) |
Depending on application, temporal texture can be viewed as either foreground or background. We address two related problems: finding regions of dynamic texture in a video and detecting moving targets in a dynamic texture. We propose efficient and fast methods for both cases. The methods can be potentially used in real-time applications of machine vision. First, we show how the optical flow residual can be used to find dynamic texture in video. The algorithm is a practical, real-time simplification of the sophisticated and powerful but time-consuming method (Fazekas et al. in Int J Comput Vis 82:48–63, 2009). We give numerous examples of detecting and segmenting fire, smoke, water and other dynamic textures in real-world videos acquired by static and moving cameras. Then we apply the singular value decomposition (SVD) to a temporal data window in a video to detect targets in dynamic texture via the residual of the largest singular value. |
| reportyear |
2012 |
| RIV |
BD |
| mrcbC52 |
4 A 4a 20231122134102.9 |
| permalink |
http://hdl.handle.net/11104/0186722 |
| mrcbT16-e |
COMPUTERSCIENCEARTIFICIALINTELLIGENCE|COMPUTERSCIENCECYBERNETICS|ENGINEERINGELECTRICALELECTRONIC |
| mrcbT16-f |
1.324 |
| mrcbT16-g |
0.132 |
| mrcbT16-h |
7.6 |
| mrcbT16-i |
0.00254 |
| mrcbT16-j |
0.669 |
| mrcbT16-k |
711 |
| mrcbT16-l |
76 |
| mrcbT16-s |
0.501 |
| mrcbT16-4 |
Q1 |
| mrcbT16-B |
60.988 |
| mrcbT16-C |
44.225 |
| mrcbT16-D |
Q2 |
| mrcbT16-E |
Q3 |
| arlyear |
2011 |
| mrcbTft |
\nSoubory v repozitáři: haindl-0345450.pdf |
| mrcbU14 |
80053895602 SCOPUS |
| mrcbU34 |
000293961900002 WOS |
| mrcbU63 |
cav_un_epca*0254218 Machine Vision and Applications 0932-8092 1432-1769 Roč. 22 č. 5 2011 741 750 Springer |
|