TY  - CONF
AU  - Xavier Soria
AU  - Edgar Riba
AU  - Angel Sappa
A2  - WACV
PY  - 2020//
TI  - Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection
BT  - IEEE Winter Conference on Applications of Computer Vision
N2  - 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.
UR  - https://ieeexplore.ieee.org/document/9093290
L1  - http://refbase.cvc.uab.es/files/SRS2020.pdf
UR  - http://dx.doi.org/10.1109/WACV45572.2020.9093290
N1  - MSIAU; 600.130; 601.349; 600.122
ID  - Xavier Soria2020
ER  -