Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection
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.
Cite
Text
Poma et al. "Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection." Winter Conference on Applications of Computer Vision, 2020.Markdown
[Poma et al. "Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection." Winter Conference on Applications of Computer Vision, 2020.](https://mlanthology.org/wacv/2020/poma2020wacv-dense/)BibTeX
@inproceedings{poma2020wacv-dense,
title = {{Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection}},
author = {Poma, Xavier Soria and Riba, Edgar and Sappa, Angel},
booktitle = {Winter Conference on Applications of Computer Vision},
year = {2020},
url = {https://mlanthology.org/wacv/2020/poma2020wacv-dense/}
}