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27 lines
1.5 KiB
ReStructuredText
27 lines
1.5 KiB
ReStructuredText
.. _dexined_model:
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Dexined (edge detection)
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------------------------
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.. card::
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:link: https://www.computer.org/csdl/proceedings-article/wacv/2020/09093290/1jPbjFHmwi4
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**Dense Extreme Inception Network for Edge Detection**
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^^^
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**Abstract:** Edge detection is the basis of many computer vision applications. State of the art predominantly relies on deep learning with two decisive factors: dataset content and network's architecture. Most of the publicly available datasets are not curated for edge detection tasks. Here, we offer a solution to this constraint. First, we argue that edges, contours and boundaries, despite their overlaps, are three distinct visual features requiring separate benchmark datasets. To this end, we present a new dataset of edges. Second, we propose a novel architecture, termed Dense Extreme Inception Network for Edge Detection (DexiNed), that can be trained from scratch without any pre-trained weights. DexiNed outperforms other algorithms in the presented dataset. It also generalizes well to other datasets without any fine-tuning. The higher quality of DexiNed is also perceptually evident thanks to the sharper and finer edges it outputs.
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**Tasks:** Edge Detection
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**Datasets:** BSD500, BIPED, MDBD
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**Journal:** 2020 IEEE Winter Conference on Applications of Computer Vision (WACV)
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**Licence:** MIT
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+++
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**Authors:** X. Soria and E. Riba and A. Sappa
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.. image:: https://github.com/xavysp/DexiNed/raw/master/figs/DexiNed_banner.png
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:align: center
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