@Article{XavierSoria2023,
author="Xavier Soria
and Angel Sappa
and Patricio Humanante
and Arash Akbarinia",
title="Dense extreme inception network for edge detection",
journal="Pattern Recognition",
year="2023",
volume="139",
pages="109461",
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 architecture. Most of the publicly available datasets are not curated for edge detection tasks. Here, we address this limitation. 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.",
optnote="MSIAU",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3982), last updated on Mon, 29 Jan 2024 10:11:09 +0100",
opturl="https://www.sciencedirect.com/science/article/abs/pii/S0031320323001619"
}