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Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12008/34071 How to cite
Title: A brief analysis of the holistically-nested edge detector
Authors: Grompone von Gioi, Rafael
Randall, Gregory
Type: Artículo
Keywords: Image edge detection, Neural network, VGG16
Issue Date: 2022
Abstract: This work describes the HED method for edge detection. HED uses a neural network based on a VGG16 backbone, supplemented with some extra layers for merging the results at different scales. The training was performed on an augmented version of the BSDS500 dataset. We perform a brief analysis of the results produced by HED, highlighting its quality but also indicating its limitations. Overall, HED produces state-of-the-art results.
Description: Este artículo está disponible en línea con materiales complementarios, software, conjuntos de datos y demostración en https://doi.org/10.5201/ipol.2022.422
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. 369-377
Citation: Grompone von Gioi, R. y Randall, G. "A brief analysis of the holistically-nested edge detector". IPOL. Journal Image Processing On Line. [en línea]. 2022, no 12, pp. 369-377. DOI: 10.5201/ipol.2022.422
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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