|
|
|
|
| LEADER |
01000caa a22002652 4500 |
| 001 |
NLM202020673 |
| 003 |
DE-627 |
| 005 |
20250212020037.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
231223s2011 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1109/TIP.2010.2081679
|2 doi
|
| 028 |
5 |
2 |
|a pubmed25n0673.xml
|
| 035 |
|
|
|a (DE-627)NLM202020673
|
| 035 |
|
|
|a (NLM)20889431
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Tošić, Ivana
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a Dictionary learning for stereo image representation
|
| 264 |
|
1 |
|c 2011
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ƒaComputermedien
|b c
|2 rdamedia
|
| 338 |
|
|
|a ƒa Online-Ressource
|b cr
|2 rdacarrier
|
| 500 |
|
|
|a Date Completed 16.08.2011
|
| 500 |
|
|
|a Date Revised 22.03.2011
|
| 500 |
|
|
|a published: Print-Electronic
|
| 500 |
|
|
|a Citation Status MEDLINE
|
| 520 |
|
|
|a One of the major challenges in multi-view imaging is the definition of a representation that reveals the intrinsic geometry of the visual information. Sparse image representations with overcomplete geometric dictionaries offer a way to efficiently approximate these images, such that the multi-view geometric structure becomes explicit in the representation. However, the choice of a good dictionary in this case is far from obvious. We propose a new method for learning overcomplete dictionaries that are adapted to the joint representation of stereo images. We first formulate a sparse stereo image model where the multi-view correlation is described by local geometric transforms of dictionary elements (atoms) in two stereo views. A maximum-likelihood (ML) method for learning stereo dictionaries is then proposed, where a multi-view geometry constraint is included in the probabilistic model. The ML objective function is optimized using the expectation-maximization algorithm. We apply the learning algorithm to the case of omnidirectional images, where we learn scales of atoms in a parametric dictionary. The resulting dictionaries provide better performance in the joint representation of stereo omnidirectional images as well as improved multi-view feature matching. We finally discuss and demonstrate the benefits of dictionary learning for distributed scene representation and camera pose estimation
|
| 650 |
|
4 |
|a Journal Article
|
| 650 |
|
4 |
|a Research Support, Non-U.S. Gov't
|
| 700 |
1 |
|
|a Frossard, Pascal
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 20(2011), 4 vom: 01. Apr., Seite 921-34
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
|
| 773 |
1 |
8 |
|g volume:20
|g year:2011
|g number:4
|g day:01
|g month:04
|g pages:921-34
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TIP.2010.2081679
|3 Volltext
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_350
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|d 20
|j 2011
|e 4
|b 01
|c 04
|h 921-34
|