@InProceedings{XavierSoria2020,
author="Xavier Soria
and Edgar Riba
and Angel Sappa",
title="Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection",
booktitle="IEEE Winter Conference on Applications of Computer Vision",
year="2020",
abstract="This paper proposes a Deep Learning based edge detector, which is inspired on both HED (Holistically-Nested Edge Detection) and Xception networks. The proposed approach generates thin edge-maps that are plausible for human eyes; it can be used in any edge detection task without previous training or fine tuning process. As a second contribution, a large dataset with carefully annotated edges has been generated. This dataset has been used for training the proposed approach as well the state-of-the-art algorithms for comparisons. Quantitative and qualitative evaluations have been performed on different benchmarks showing improvements with the proposed method when F-measure of ODS and OIS are considered.",
optnote="MSIAU; 600.130; 601.349; 600.122",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3434), last updated on Thu, 11 Feb 2021 07:38:52 +0100",
doi="10.1109/WACV45572.2020.9093290",
opturl="https://ieeexplore.ieee.org/document/9093290",
file=":http://refbase.cvc.uab.es/files/SRS2020.pdf:PDF"
}