A brief analysis of the dense extreme inception network for edge detection
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.
| 2022 | |
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Image edge detection Neural network HED Xception |
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| Inglés | |
| Universidad de la República | |
| COLIBRI | |
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https://www.ipol.im/pub/art/2022/423/
https://hdl.handle.net/20.500.12008/34134 |
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| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Compartir Igual (CC - By-NC-SA 4.0) |
| _version_ | 1807522899469271040 |
|---|---|
| author | Grompone von Gioi, Rafael |
| author2 | Randall, Gregory |
| author2_role | author |
| author_facet | Grompone von Gioi, Rafael Randall, Gregory |
| author_role | author |
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| collection | COLIBRI |
| 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.creator.none.fl_str_mv | Grompone von Gioi, Rafael Randall, Gregory |
| dc.date.accessioned.none.fl_str_mv | 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 |
| dc.description.abstract.none.fl_txt_mv | 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. |
| dc.format.extent.es.fl_str_mv | 15 p. |
| dc.format.mimetype.es.fl_str_mv | application/pdf |
| dc.identifier.citation.es.fl_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 |
| dc.identifier.doi.none.fl_str_mv | 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.language.iso.none.fl_str_mv | en eng |
| dc.publisher.es.fl_str_mv | Centre Borelli, ENS Paris-Saclay; DMI, Universitat de les Illes Balears; Fing, Universidad de la República. |
| dc.relation.ispartof.es.fl_str_mv | IPOL. Journal Image Processing On Line, no 12, Oct 2022, pp. 389-403. |
| dc.rights.license.none.fl_str_mv | Licencia Creative Commons Atribución - No Comercial - Compartir Igual (CC - By-NC-SA 4.0) |
| dc.rights.none.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.source.none.fl_str_mv | reponame:COLIBRI instname:Universidad de la República instacron:Universidad de la República |
| dc.subject.es.fl_str_mv | Image edge detection Neural network HED Xception |
| dc.title.none.fl_str_mv | A brief analysis of the dense extreme inception network for edge detection |
| dc.type.es.fl_str_mv | Artículo |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/article |
| dc.type.version.none.fl_str_mv | info:eu-repo/semantics/publishedVersion |
| 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. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | COLIBRI_3f2d8d64f83c59d301ed8cf6fc01acaa |
| 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 |
| instacron_str | Universidad de la República |
| institution | Universidad de la República |
| instname_str | Universidad de la República |
| language | eng |
| language_invalid_str_mv | en |
| network_acronym_str | COLIBRI |
| network_name_str | COLIBRI |
| oai_identifier_str | oai:colibri.udelar.edu.uy:20.500.12008/34134 |
| publishDate | 2022 |
| reponame_str | COLIBRI |
| repository.mail.fl_str_mv | mabel.seroubian@seciu.edu.uy |
| repository.name.fl_str_mv | COLIBRI - Universidad de la República |
| repository_id_str | 4771 |
| rights_invalid_str_mv | Licencia Creative Commons Atribución - No Comercial - Compartir Igual (CC - By-NC-SA 4.0) |
| spelling | 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). No. of bitstreams: 2 license_rdf: 23749 bytes, checksum: 6a69abe32f6fabdffa4c61be8f8efebd (MD5) GR22a.pdf: 29240450 bytes, checksum: 9a119fc5dfa819bf65578e48b2f20d8f (MD5) Previous issue date: 202215 p.application/pdfenengCentre Borelli, ENS Paris-Saclay; DMI, Universitat de les Illes Balears; Fing, Universidad de la República.IPOL. Journal Image Processing On Line, no 12, Oct 2022, pp. 389-403.Las obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad de la República.(Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014)info:eu-repo/semantics/openAccessLicencia Creative Commons Atribución - No Comercial - Compartir Igual (CC - By-NC-SA 4.0)Image edge detectionNeural networkHEDXceptionA brief analysis of the dense extreme inception network for edge detectionArtículoinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaGrompone von Gioi, RafaelRandall, GregoryProcesamiento de SeñalesTratamiento de ImágenesLICENSElicense.txtlicense.txttext/plain; charset=utf-84267http://localhost:8080/xmlui/bitstream/20.500.12008/34134/5/license.txt6429389a7df7277b72b7924fdc7d47a9MD55CC-LICENSElicense_urllicense_urltext/plain; charset=utf-850http://localhost:8080/xmlui/bitstream/20.500.12008/34134/2/license_urla9ac1bac94fe38dbe560422d834a993fMD52license_textlicense_texttext/html; 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- Universidad de la Repúblicafalse |
| spellingShingle | A brief analysis of the dense extreme inception network for edge detection Grompone von Gioi, Rafael Image edge detection Neural network HED Xception |
| status_str | publishedVersion |
| title | 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_short | 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 |
| topic | Image edge detection Neural network HED Xception |
| url | https://www.ipol.im/pub/art/2022/423/ https://hdl.handle.net/20.500.12008/34134 |
