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

2019-09-04 17:27:46
Xavier Soria, Edgar Riba, Angel D. Sappa
arXiv_CV
             
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 as 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.

Abstract (translated)

URL

https://arxiv.org/abs/1909.01955

PDF

https://arxiv.org/pdf/1909.01955.pdf

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