A Brief Analysis of the Dense Extreme Inception Network for Edge Detection
Rafael Grompone von Gioi, Gregory Randall
published
2022-10-10
reference
Rafael Grompone von Gioi, and Gregory Randall, A Brief Analysis of the Dense Extreme Inception Network for Edge Detection, Image Processing On Line, 12 (2022), pp. 389–403. https://doi.org/10.5201/ipol.2022.423

Communicated by Thibaud Ehret
Demo edited by Rafael Grompone von Gioi

Abstract

This work describes DexiNed, a Dense Extreme Inception Network for Edge Detection proposed by Xavier Soria, Edgar Riba and Angel Sappa in [IEEE Winter Conference on Applications of Computer Vision (WACV), 2020]. The network is organized in blocks that extract edges at different resolutions, which are then merged to produce a multiscale edge map. For training, the authors introduced an annotated dataset (BIPED) specifically designed for edge detection. We perform a brief analysis of the results produced by DexiNed, highlighting its quality but also indicating its limitations. Overall, DexiNed produces state-of-the-art results.

This is an MLBriefs article, the source code has not been reviewed!
The original source code is available here (last checked 2022/10/07).

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