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Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12008/34134 How to cite
Title: A brief analysis of the dense extreme inception network for edge detection
Authors: Grompone von Gioi, Rafael
Randall, Gregory
Type: Artículo
Keywords: Image edge detection, Neural network, HED, Xception
Issue Date: 2022
Abstract: 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.
Publisher: Centre Borelli, ENS Paris-Saclay; DMI, Universitat de les Illes Balears; Fing, Universidad de la República.
IN: IPOL. Journal Image Processing On Line, no 12, Oct 2022, pp. 389-403.
Citation: 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
ISSN: 2105–1232
Academic department: Procesamiento de Señales
Investigation group: Tratamiento de Imágenes
License: Licencia Creative Commons Atribución - No Comercial - Compartir Igual (CC - By-NC-SA 4.0)
Appears in Collections:Publicaciones académicas y científicas - Instituto de Ingeniería Eléctrica

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