|
|
|
|
| LEADER |
01000naa a22002652 4500 |
| 001 |
NLM362700745 |
| 003 |
DE-627 |
| 005 |
20231226091759.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
231226s2023 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1109/TIP.2023.3318963
|2 doi
|
| 028 |
5 |
2 |
|a pubmed24n1208.xml
|
| 035 |
|
|
|a (DE-627)NLM362700745
|
| 035 |
|
|
|a (NLM)37773901
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a She, Rui
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a RobustMat
|b Neural Diffusion for Street Landmark Patch Matching Under Challenging Environments
|
| 264 |
|
1 |
|c 2023
|
| 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 01.11.2023
|
| 500 |
|
|
|a published: Print-Electronic
|
| 500 |
|
|
|a Citation Status PubMed-not-MEDLINE
|
| 520 |
|
|
|a For autonomous vehicles (AVs), visual perception techniques based on sensors like cameras play crucial roles in information acquisition and processing. In various computer perception tasks for AVs, it may be helpful to match landmark patches taken by an onboard camera with other landmark patches captured at a different time or saved in a street scene image database. To perform matching under challenging driving environments caused by changing seasons, weather, and illumination, we utilize the spatial neighborhood information of each patch. We propose an approach, named RobustMat, which derives its robustness to perturbations from neural differential equations. A convolutional neural ODE diffusion module is used to learn the feature representation for the landmark patches. A graph neural PDE diffusion module then aggregates information from neighboring landmark patches in the street scene. Finally, feature similarity learning outputs the final matching score. Our approach is evaluated on several street scene datasets and demonstrated to achieve state-of-the-art matching results under environmental perturbations
|
| 650 |
|
4 |
|a Journal Article
|
| 700 |
1 |
|
|a Kang, Qiyu
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Wang, Sijie
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Yang, Yuan-Rui
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Zhao, Kai
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Song, Yang
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Tay, Wee Peng
|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 32(2023) vom: 29., Seite 5550-5563
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
|
| 773 |
1 |
8 |
|g volume:32
|g year:2023
|g day:29
|g pages:5550-5563
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TIP.2023.3318963
|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 32
|j 2023
|b 29
|h 5550-5563
|