Internal-External Boundary Attention Fusion for Glass Surface Segmentation
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
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| _version_ | 1866916145516249088 |
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| author | Han, Dongshen Lee, Seungkyu Zhang, Chaoning Yoon, Heechan Kwon, Hyukmin Kim, Hyun-Cheol Choo, Hyon-Gon |
| author_facet | Han, Dongshen Lee, Seungkyu Zhang, Chaoning Yoon, Heechan Kwon, Hyukmin Kim, Hyun-Cheol Choo, Hyon-Gon |
| contents | Glass surfaces of transparent objects and mirrors are not able to be uniquely and explicitly characterized by their visual appearances because they contain the visual appearance of other reflected or transmitted surfaces as well. Detecting glass regions from a single-color image is a challenging task. Recent deep-learning approaches have paid attention to the description of glass surface boundary where the transition of visual appearances between glass and non-glass surfaces are observed. In this work, we analytically investigate how glass surface boundary helps to characterize glass objects. Inspired by prior semantic segmentation approaches with challenging image types such as X-ray or CT scans, we propose separated internal-external boundary attention modules that individually learn and selectively integrate visual characteristics of the inside and outside region of glass surface from a single color image. Our proposed method is evaluated on six public benchmarks comparing with state-of-the-art methods showing promising results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_00212 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Internal-External Boundary Attention Fusion for Glass Surface Segmentation Han, Dongshen Lee, Seungkyu Zhang, Chaoning Yoon, Heechan Kwon, Hyukmin Kim, Hyun-Cheol Choo, Hyon-Gon Computer Vision and Pattern Recognition Glass surfaces of transparent objects and mirrors are not able to be uniquely and explicitly characterized by their visual appearances because they contain the visual appearance of other reflected or transmitted surfaces as well. Detecting glass regions from a single-color image is a challenging task. Recent deep-learning approaches have paid attention to the description of glass surface boundary where the transition of visual appearances between glass and non-glass surfaces are observed. In this work, we analytically investigate how glass surface boundary helps to characterize glass objects. Inspired by prior semantic segmentation approaches with challenging image types such as X-ray or CT scans, we propose separated internal-external boundary attention modules that individually learn and selectively integrate visual characteristics of the inside and outside region of glass surface from a single color image. Our proposed method is evaluated on six public benchmarks comparing with state-of-the-art methods showing promising results. |
| title | Internal-External Boundary Attention Fusion for Glass Surface Segmentation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2307.00212 |