|
|
|
|
| LEADER |
01000naa a22002652 4500 |
| 001 |
NLM338504540 |
| 003 |
DE-627 |
| 005 |
20231226000824.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
231226s2022 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1109/TIP.2022.3160399
|2 doi
|
| 028 |
5 |
2 |
|a pubmed24n1128.xml
|
| 035 |
|
|
|a (DE-627)NLM338504540
|
| 035 |
|
|
|a (NLM)35320103
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Zhang, Xiang
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a Guided Filter Network for Semantic Image Segmentation
|
| 264 |
|
1 |
|c 2022
|
| 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 Revised 30.03.2022
|
| 500 |
|
|
|a published: Print-Electronic
|
| 500 |
|
|
|a Citation Status PubMed-not-MEDLINE
|
| 520 |
|
|
|a The existing publicly available datasets with pixel-level labels contain limited categories, and it is difficult to generalize to the real world containing thousands of categories. In this paper, we propose an approach to generate object masks with detailed pixel-level structures/boundaries automatically to enable semantic image segmentation of thousands of targets in the real world without manually labelling. A Guided Filter Network (GFN) is first developed to learn the segmentation knowledge from an existed dataset, and such GFN then transfers the learned segmentation knowledge to generate initial coarse object masks for the target images. These coarse object masks are treated as pseudo labels to self-optimize the GFN iteratively in the target images. Our experiments on six image sets have demonstrated that our proposed approach can generate object masks with detailed pixel-level structures/boundaries, whose quality is comparable to the manually-labelled ones. Our proposed approach also achieves better performance on semantic image segmentation than most existing weakly-supervised, semi-supervised, and domain adaptation approaches under the same experimental conditions
|
| 650 |
|
4 |
|a Journal Article
|
| 700 |
1 |
|
|a Zhao, Wanqing
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Zhang, Wei
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Peng, Jinye
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Fan, Jianping
|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 31(2022) vom: 02., Seite 2695-2709
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
|
| 773 |
1 |
8 |
|g volume:31
|g year:2022
|g day:02
|g pages:2695-2709
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TIP.2022.3160399
|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 31
|j 2022
|b 02
|h 2695-2709
|