| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0542259 |
| utime |
20240103225755.7 |
| mtime |
20210511235959.9 |
| DOI |
10.1007/978-3-030-75549-2_16 |
| title
(primary) (eng) |
First-order geometric multilevel optimization for discrete tomography |
| specification |
| page_count |
13 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0542258 |
| ISBN |
978-3-030-75549-2 |
| title
|
Scale Space and Variational Methods in Computer Vision: 8th International Conference, SSVM 2021 |
| page_num |
191-203 |
| publisher |
| place |
Cham |
| name |
Springer |
| year |
2021 |
|
|
| keyword |
discrete tomography |
| keyword |
multilevel optimization |
| keyword |
n-orthotope |
| author
(primary) |
| ARLID |
cav_un_auth*0408933 |
| name1 |
Plier |
| name2 |
J. |
| country |
DE |
|
| author
|
| ARLID |
cav_un_auth*0408934 |
| name1 |
Savarino |
| name2 |
F. |
| country |
DE |
|
| author
|
| ARLID |
cav_un_auth*0101131 |
| name1 |
Kočvara |
| name2 |
Michal |
| institution |
UTIA-B |
| full_dept (cz) |
Matematická teorie rozhodování |
| full_dept |
Department of Decision Making Theory |
| department (cz) |
MTR |
| department |
MTR |
| full_dept |
Department of Decision Making Theory |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0408935 |
| name1 |
Petra |
| name2 |
S. |
| country |
DE |
|
| source |
|
| cas_special |
| abstract
(eng) |
Discrete tomography (DT) naturally leads to a hierarchy of models of varying discretization levels. We employ multilevel optimization (MLO) to take advantage of this hierarchy: while working at the fine level we compute the search direction based on a coarse model. Importing concepts from information geometry to the n-orthotope, we propose a smoothing operator that only uses first-order information and incorporates constraints smoothly. We show that the proposed algorithm is well suited to the ill-posed reconstruction problem in DT, compare it to a recent MLO method that nonsmoothly incorporates box constraints and demonstrate its efficiency on several large-scale examples. |
| action |
| ARLID |
cav_un_auth*0410997 |
| name |
International Conference on Scale Space and Variational Methods in Computer Vision : SSVM 2021 /8./ |
| dates |
20210516 |
| mrcbC20-s |
20210520 |
| place |
Virtual Event |
| country |
CH |
|
| RIV |
BA |
| FORD0 |
10000 |
| FORD1 |
10100 |
| FORD2 |
10102 |
| reportyear |
2022 |
| num_of_auth |
4 |
| presentation_type |
PR |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0320772 |
| confidential |
S |
| arlyear |
2021 |
| mrcbU14 |
SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
WOS |
| mrcbU63 |
cav_un_epca*0542258 Scale Space and Variational Methods in Computer Vision: 8th International Conference, SSVM 2021 978-3-030-75549-2 191 203 Cham Springer 2021 Lecture Notes in Computer Science 12679 |
|