A brief analysis of the dense extreme inception network for edge detection
This work describes DexiNed, a Dense Extreme Inception Network for Edge Detection proposed by Xavier Soria, Edgar Riba and Angel Sappa in [IEEE Winter Conference on Applications of Computer Vision (WACV), 2020]. The network is organized in blocks that extract edges at different resolutions, which ar...
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| Үндсэн зохиолч: | |
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| Бусад зохиолчид: | |
| Формат: | article |
| Хэл сонгох: | англи |
| Хэвлэсэн: |
2022
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| Нөхцлүүд: | |
| Онлайн хандалт: | https://www.ipol.im/pub/art/2022/423/ https://hdl.handle.net/20.500.12008/34134 |
| Шошгууд: |
Шошго байхгүй, Энэхүү баримтыг шошголох эхний хүн болох!
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| Тойм: | This work describes DexiNed, a Dense Extreme Inception Network for Edge Detection proposed by Xavier Soria, Edgar Riba and Angel Sappa in [IEEE Winter Conference on Applications of Computer Vision (WACV), 2020]. The network is organized in blocks that extract edges at different resolutions, which are then merged to produce a multiscale edge map. For training, the authors introduced an annotated dataset (BIPED) specifically designed for edge detection. We perform a brief analysis of the results produced by DexiNed, highlighting its quality but also indicating its limitations. Overall, DexiNed produces state-of-the-art results. |
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