Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection

Xavier Soria Poma, Edgar Riba, Angel Sappa; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2020, pp. 1923-1932

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.

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[bibtex]
@InProceedings{Poma_2020_WACV,
author = {Poma, Xavier Soria and Riba, Edgar and Sappa, Angel},
title = {Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {March},
year = {2020}
}