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Internal-external boundary attention fusion for glass surface segmentation
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- Authors
- Dongshen Han, Heechan Yoon, Hyukmin Kwon, Hyun-Cheol Kim, Hyon-Gon Choo, Seungkyu Lee, Chaoning Zhang
- Issue Date
- 2026-03
- Citation
- Neural Networks, v.195, pp.1-10
- ISSN
- 0893-6080
- Publisher
- Elsevier
- Language
- English
- Type
- Journal Article
- Abstract
- Detecting transparent objects and mirrors in an image is a highly challenging task because their glass surfaces contain the visual appearance of other transmitted or reflected objects. In this work, we highlight the importance of exploiting the transition region between glass and non-glass surfaces, which is called boundary. In other words, we exploit the visual appearances of the transition region to facilitate the task of glass surface segmentation. Specifically, we divide the transition region into internal boundary and external boundary and propose an internal-external boundary attention module (IEBAM) to separately learn their visual characteristics. The processed features are then integrated dynamically via a fusion boundary attention module (FBAM). Extensible results on six benchmark datasets show that our proposed IEBAM and FBAM are effective in improving the performance of glass surface segmentation.
- Keyword
- Attention, Boundary, Glass surface
- KSP Keywords
- Benchmark datasets, Fusion boundary, Glass surface, Internal boundary, Surface segmentation, Transition region, Visual appearance, attention fusion, transparent objects, visual characteristics