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231226s2023 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2023.3281171
|2 doi
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|a DE-627
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|e rakwb
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|a eng
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| 100 |
1 |
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|a She, Rui
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Image Patch-Matching With Graph-Based Learning in Street Scenes
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|c 2023
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|a Text
|b txt
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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| 500 |
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|a Date Completed 26.06.2023
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|a Date Revised 26.06.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Matching landmark patches from a real-time image captured by an on-vehicle camera with landmark patches in an image database plays an important role in various computer perception tasks for autonomous driving. Current methods focus on local matching for regions of interest and do not take into account spatial neighborhood relationships among the image patches, which typically correspond to objects in the environment. In this paper, we construct a spatial graph with the graph vertices corresponding to patches and edges capturing the spatial neighborhood information. We propose a joint feature and metric learning model with graph-based learning. We provide a theoretical basis for the graph-based loss by showing that the information distance between the distributions conditioned on matched and unmatched pairs is maximized under our framework. We evaluate our model using several street-scene datasets and demonstrate that our approach achieves state-of-the-art matching results
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|a Journal Article
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| 700 |
1 |
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|a Kang, Qiyu
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Sijie
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Tay, Wee Peng
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Guan, Yong Liang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Navarro, Diego Navarro
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Hartmannsgruber, Andreas
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 32(2023) vom: 05., Seite 3465-3480
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|g volume:32
|g year:2023
|g day:05
|g pages:3465-3480
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|u http://dx.doi.org/10.1109/TIP.2023.3281171
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