|
|
|
|
| LEADER |
01000naa a22002652 4500 |
| 001 |
NLM228640555 |
| 003 |
DE-627 |
| 005 |
20231224080405.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
231224s2013 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1109/TIP.2013.2263147
|2 doi
|
| 028 |
5 |
2 |
|a pubmed24n0762.xml
|
| 035 |
|
|
|a (DE-627)NLM228640555
|
| 035 |
|
|
|a (NLM)23799695
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Gao, Yi
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a Sparse texture active contour
|
| 264 |
|
1 |
|c 2013
|
| 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 01.04.2014
|
| 500 |
|
|
|a Date Revised 21.10.2021
|
| 500 |
|
|
|a published: Print-Electronic
|
| 500 |
|
|
|a Citation Status PubMed-not-MEDLINE
|
| 520 |
|
|
|a In image segmentation, we are often interested in using certain quantities to characterize the object, and perform the classification based on criteria such as mean intensity, gradient magnitude, and responses to certain predefined filters. Unfortunately, in many cases such quantities are not adequate to model complex textured objects. Along a different line of research, the sparse characteristic of natural signals has been recognized and studied in recent years. Therefore, how such sparsity can be utilized, in a non-parametric way, to model the object texture and assist the textural image segmentation process is studied in this paper, and a segmentation scheme based on the sparse representation of the texture information is proposed. More explicitly, the texture is encoded by the dictionaries constructed from the user initialization. Then, an active contour is evolved to optimize the fidelity of the representation provided by the dictionary of the target. In doing so, not only a non-parametric texture modeling technique is provided, but also the sparsity of the representation guarantees the computation efficiency. The experiments are carried out on the publicly available image data sets which contain a large variety of texture images, to analyze the user interaction, performance statistics, and to highlight the algorithm's capability of robustly extracting textured regions from an image
|
| 650 |
|
4 |
|a Journal Article
|
| 650 |
|
4 |
|a Research Support, N.I.H., Extramural
|
| 650 |
|
4 |
|a Research Support, U.S. Gov't, Non-P.H.S.
|
| 700 |
1 |
|
|a Bouix, Sylvain
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Shenton, Martha
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Tannenbaum, Allen
|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 22(2013), 10 vom: 01. Okt., Seite 3866-78
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
|
| 773 |
1 |
8 |
|g volume:22
|g year:2013
|g number:10
|g day:01
|g month:10
|g pages:3866-78
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TIP.2013.2263147
|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 22
|j 2013
|e 10
|b 01
|c 10
|h 3866-78
|