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| Título: | A brief analysis of the dense extreme inception network for edge detection |
| Autor: | Grompone von Gioi, Rafael Randall, Gregory |
| Tipo: | Artículo |
| Palabras clave: | Image edge detection, Neural network, HED, Xception |
| Fecha de publicación: | 2022 |
| Resumen: | 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. |
| Editorial: | Centre Borelli, ENS Paris-Saclay; DMI, Universitat de les Illes Balears; Fing, Universidad de la República. |
| EN: | IPOL. Journal Image Processing On Line, no 12, Oct 2022, pp. 389-403. |
| DOI: | 10.5201/ipol.2022.423 |
| ISSN: | 2105–1232 |
| Citación: | Grompone von Gioi, R y Randall, G. "A brief analysis of the dense extreme inception network for edge detection". IPOL. Journal Image Processing On Line. [en línea]. 2022, no 12, pp. 389-403. DOI: 10.5201/ipol.2022.423 |
| Licencia: | Licencia Creative Commons Atribución - No Comercial - Compartir Igual (CC - By-NC-SA 4.0) |
| Aparece en las colecciones: | Publicaciones académicas y científicas - Instituto de Ingeniería Eléctrica |
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| Fichero | Descripción | Tamaño | Formato | ||
|---|---|---|---|---|---|
| GR22a.pdf | Versión publicada | 28,56 MB | Adobe PDF | Visualizar/Abrir |
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