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...
| Autores: | , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2022 |
| País: | Uruguay |
| Institución: | Universidad de la República |
| Repositorio: | COLIBRI |
| Idioma: | inglés |
| OAI Identifier: | oai:colibri.udelar.edu.uy:20.500.12008/34134 |
| Acceso en línea: | https://www.ipol.im/pub/art/2022/423/ https://hdl.handle.net/20.500.12008/34134 |
| Access Level: | acceso abierto |
| Palabra clave: | Image edge detection Neural network HED Xception |
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A brief analysis of the dense extreme inception network for edge detection |
| title |
A brief analysis of the dense extreme inception network for edge detection |
| spellingShingle |
A brief analysis of the dense extreme inception network for edge detection Grompone von Gioi, Rafael Image edge detection Neural network HED Xception |
| title_short |
A brief analysis of the dense extreme inception network for edge detection |
| title_full |
A brief analysis of the dense extreme inception network for edge detection |
| title_fullStr |
A brief analysis of the dense extreme inception network for edge detection |
| title_full_unstemmed |
A brief analysis of the dense extreme inception network for edge detection |
| title_sort |
A brief analysis of the dense extreme inception network for edge detection |
| dc.creator.none.fl_str_mv |
Grompone von Gioi, Rafael Randall, Gregory |
| author |
Grompone von Gioi, Rafael |
| author_facet |
Grompone von Gioi, Rafael Randall, Gregory |
| author_role |
author |
| author2 |
Randall, Gregory |
| author2_role |
author |
| dc.contributor.filiacion.none.fl_str_mv |
Grompone von Gioi Rafael, Université Paris-Saclay, France Randall Gregory, Universidad de la República (Uruguay). Facultad de Ingeniería. |
| dc.subject.es.fl_str_mv |
Image edge detection Neural network HED Xception |
| topic |
Image edge detection Neural network HED Xception |
| description |
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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2022 |
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2022-10-13T12:25:16Z |
| dc.date.available.none.fl_str_mv |
2022-10-13T12:25:16Z |
| dc.date.issued.none.fl_str_mv |
2022 |
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Artículo |
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info:eu-repo/semantics/article |
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info:eu-repo/semantics/publishedVersion |
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article |
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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 |
| dc.identifier.issn.none.fl_str_mv |
2105–1232 |
| dc.identifier.uri.none.fl_str_mv |
https://www.ipol.im/pub/art/2022/423/ https://hdl.handle.net/20.500.12008/34134 |
| dc.identifier.doi.none.fl_str_mv |
10.5201/ipol.2022.423 |
| identifier_str_mv |
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 2105–1232 10.5201/ipol.2022.423 |
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https://www.ipol.im/pub/art/2022/423/ https://hdl.handle.net/20.500.12008/34134 |
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en eng |
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en |
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eng |
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IPOL. Journal Image Processing On Line, no 12, Oct 2022, pp. 389-403. |
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info:eu-repo/semantics/openAccess |
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Licencia Creative Commons Atribución - No Comercial - Compartir Igual (CC - By-NC-SA 4.0) |
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openAccess |
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Licencia Creative Commons Atribución - No Comercial - Compartir Igual (CC - By-NC-SA 4.0) |
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Centre Borelli, ENS Paris-Saclay; DMI, Universitat de les Illes Balears; Fing, Universidad de la República. |
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Grompone von Gioi Rafael, Université Paris-Saclay, FranceRandall Gregory, Universidad de la República (Uruguay). Facultad de Ingeniería.2022-10-13T12:25:16Z2022-10-13T12:25:16Z2022Grompone 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.2022.4232105–1232https://www.ipol.im/pub/art/2022/423/https://hdl.handle.net/20.500.12008/3413410.5201/ipol.2022.423This 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.Submitted by Ribeiro Jorge (jribeiro@fing.edu.uy) on 2022-10-11T19:07:10Z No. of bitstreams: 2 license_rdf: 23749 bytes, checksum: 6a69abe32f6fabdffa4c61be8f8efebd (MD5) GR22a.pdf: 29240450 bytes, checksum: 9a119fc5dfa819bf65578e48b2f20d8f (MD5)Approved for entry into archive by Machado Jimena (jmachado@fing.edu.uy) on 2022-10-12T18:23:50Z (GMT) No. of bitstreams: 2 license_rdf: 23749 bytes, checksum: 6a69abe32f6fabdffa4c61be8f8efebd (MD5) GR22a.pdf: 29240450 bytes, checksum: 9a119fc5dfa819bf65578e48b2f20d8f (MD5)Made available in DSpace by Luna Fabiana (fabiana.luna@seciu.edu.uy) on 2022-10-13T12:25:16Z (GMT). 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