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|a 10.1109/TPAMI.2022.3181973
|2 doi
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|a DE-627
|b ger
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Mei, Haiyang
|e verfasserin
|4 aut
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| 245 |
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|a Large-Field Contextual Feature Learning for Glass Detection
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|c 2023
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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| 500 |
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|a Date Completed 07.04.2023
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|a Date Revised 11.04.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Glass is very common in our daily life. Existing computer vision systems neglect it and thus may have severe consequences, e.g., a robot may crash into a glass wall. However, sensing the presence of glass is not straightforward. The key challenge is that arbitrary objects/scenes can appear behind the glass. In this paper, we propose an important problem of detecting glass surfaces from a single RGB image. To address this problem, we construct the first large-scale glass detection dataset (GDD) and propose a novel glass detection network, called GDNet-B, which explores abundant contextual cues in a large field-of-view via a novel large-field contextual feature integration (LCFI) module and integrates both high-level and low-level boundary features with a boundary feature enhancement (BFE) module. Extensive experiments demonstrate that our GDNet-B achieves satisfying glass detection results on the images within and beyond the GDD testing set. We further validate the effectiveness and generalization capability of our proposed GDNet-B by applying it to other vision tasks, including mirror segmentation and salient object detection. Finally, we show the potential applications of glass detection and discuss possible future research directions
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|a Journal Article
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| 700 |
1 |
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|a Yang, Xin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yu, Letian
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Qiang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wei, Xiaopeng
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Lau, Rynson W H
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 45(2023), 3 vom: 19. März, Seite 3329-3346
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|x 1939-3539
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|g volume:45
|g year:2023
|g number:3
|g day:19
|g month:03
|g pages:3329-3346
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|u http://dx.doi.org/10.1109/TPAMI.2022.3181973
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