|
|
|
|
| LEADER |
01000naa a22002652 4500 |
| 001 |
NLM214428869 |
| 003 |
DE-627 |
| 005 |
20231224023553.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
231224s2012 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1109/TPAMI.2012.15
|2 doi
|
| 028 |
5 |
2 |
|a pubmed24n0715.xml
|
| 035 |
|
|
|a (DE-627)NLM214428869
|
| 035 |
|
|
|a (NLM)22231594
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Gopalan, Raghuraman
|e verfasserin
|4 aut
|
| 245 |
1 |
2 |
|a A blur-robust descriptor with applications to face recognition
|
| 264 |
|
1 |
|c 2012
|
| 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 17.12.2012
|
| 500 |
|
|
|a Date Revised 29.06.2012
|
| 500 |
|
|
|a published: Print
|
| 500 |
|
|
|a Citation Status MEDLINE
|
| 520 |
|
|
|a Understanding the effect of blur is an important problem in unconstrained visual analysis. We address this problem in the context of image-based recognition by a fusion of image-formation models and differential geometric tools. First, we discuss the space spanned by blurred versions of an image and then, under certain assumptions, provide a differential geometric analysis of that space. More specifically, we create a subspace resulting from convolution of an image with a complete set of orthonormal basis functions of a prespecified maximum size (that can represent an arbitrary blur kernel within that size), and show that the corresponding subspaces created from a clean image and its blurred versions are equal under the ideal case of zero noise and some assumptions on the properties of blur kernels. We then study the practical utility of this subspace representation for the problem of direct recognition of blurred faces by viewing the subspaces as points on the Grassmann manifold and present methods to perform recognition for cases where the blur is both homogenous and spatially varying. We empirically analyze the effect of noise, as well as the presence of other facial variations between the gallery and probe images, and provide comparisons with existing approaches on standard data sets
|
| 650 |
|
4 |
|a Journal Article
|
| 650 |
|
4 |
|a Research Support, U.S. Gov't, Non-P.H.S.
|
| 700 |
1 |
|
|a Taheri, Sima
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Turaga, Pavan
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Chellappa, Rama
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 34(2012), 6 vom: 14. Juni, Seite 1220-6
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
|
| 773 |
1 |
8 |
|g volume:34
|g year:2012
|g number:6
|g day:14
|g month:06
|g pages:1220-6
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TPAMI.2012.15
|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 34
|j 2012
|e 6
|b 14
|c 06
|h 1220-6
|