A brief analysis of the holistically-nested edge detector
Resumen:
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.
| 2022 | |
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Image edge detection Neural network VGG16 |
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| Inglés | |
| Universidad de la República | |
| COLIBRI | |
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https://www.ipol.im/pub/art/2022/422/
https://hdl.handle.net/20.500.12008/34071 |
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| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Compartir Igual (CC - By-NC-SA 4.0) |
| Sumario: | Este artículo está disponible en línea con materiales complementarios, software, conjuntos de datos y demostración en https://doi.org/10.5201/ipol.2022.422 |
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