| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0083357 |
| utime |
20240103184231.7 |
| mtime |
20070618235959.9 |
| title
(primary) (eng) |
A Hierarchical Finite-State Model for Texture Segmentation |
| specification |
|
| serial |
| ARLID |
cav_un_epca*0083356 |
| ISSN |
1520-6149 |
| title
|
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'07) /32./ |
| part_title |
1 |
| page_num |
1209-1212 |
| publisher |
| place |
Los Alamos |
| name |
IEEE |
| year |
2007 |
|
|
| title
(cze) |
Hierarchický model s konečnými stavy pro segmentaci textur |
| keyword |
image segmentation |
| keyword |
texture |
| keyword |
Markov random fields |
| author
(primary) |
| ARLID |
cav_un_auth*0216377 |
| name1 |
Scarpa |
| name2 |
G. |
| country |
IT |
|
| author
|
| ARLID |
cav_un_auth*0101093 |
| name1 |
Haindl |
| name2 |
Michal |
| institution |
UTIA-B |
| full_dept |
Department of Pattern Recognition |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0226576 |
| name1 |
Zerubia |
| name2 |
J. |
| country |
FR |
|
| cas_special |
| project |
| project_id |
1ET400750407 |
| agency |
GA AV ČR |
| ARLID |
cav_un_auth*0001797 |
|
| project |
| project_id |
507752 |
| country |
XE |
| agency |
EC |
| ARLID |
cav_un_auth*0200689 |
|
| research |
CEZ:AV0Z10750506 |
| abstract
(eng) |
A novel model for unsupervised segmentation of texture images is presented. The image to be segmented is first discretized and then a hierarchical finite-state region-based model is automatically coupled with the data by means of a sequential optimization scheme, namely the Texture Fragmentation and Reconstruction (TFR) algorithm. Both intra- and inter-texture interactions are modeled, by means of an underlying hierarchical finite-state model, and eventually the segmentation task is addressed in a completely unsupervised manner. The output is then a nested segmentation, so that the user may decide the scale at which the segmentation has to be provided. TFR is composed of two steps: the former focuses on the estimation of the states at the finest level of the hierarchy, and is associated with an image fragmentation, or over-segmentation; the latter deals with the reconstruction of the hierarchy representing the textural interaction at different scales. |
| abstract
(cze) |
Nový model neřízené segmentace texturních obrazů je studován v článku. Segmentovaný obraz se nejprve diskretizuje a potom hierarchický model s konečnými stavy je automaticky naučen na datech pomocí sekvenčního optimalizačního algoritmu nazvaného Texture Fragmentation and Reconstruction (TFR) algoritmus. Jak intra, tak inter texturní interakce jsou modelovány pomocí tohoto hierarchického modelu s konečnými stavy a segmentace je uskutečněna zcela neřízeným způsobem. Výsledkem je hierarchická segmentace, kde se uživatel může rozhodnout pro její měřítko. TFR se skládá ze dvou kroků, odhadu stavů a obrazové fragmentace. |
| action |
| ARLID |
cav_un_auth*0228165 |
| name |
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'07) /32./ |
| place |
Honolulu |
| dates |
15.04.2007-20.04.2007 |
| country |
US |
|
| reportyear |
2008 |
| RIV |
BD |
| permalink |
http://hdl.handle.net/11104/0146621 |
| arlyear |
2007 |
| mrcbU63 |
cav_un_epca*0083356 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'07) /32./ 1 1520-6149 1209 1212 Los Alamos IEEE 2007 |
|