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Author |
Xavier Soria, Angel Sappa, Patricio Humanante, Arash Akbarinia |
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Title |
Dense extreme inception network for edge detection. |
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Journal Article |
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2023 |
Publication |
Pattern Recognition |
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Vol. 139 |
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00313203 |
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no |
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Call Number |
cidis @ cidis @ |
Serial |
216 |
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Author |
Xavier Soria, Yachuan Li, Mohammad Rouhani & Angel D. Sappa |
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Title |
Tiny and Efficient Model for the Edge Detection Generalization |
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Conference Article |
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Year |
2023 |
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Proceedings – 2023 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2023 |
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1356 - 1365 |
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no |
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Call Number |
cidis @ cidis @ |
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229 |
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Author |
Xavier Soria , Gonzalo Pomboza-Junez & Angel Sappa. |
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Title |
LDC: Lightweight Dense CNN for Edge Detection. |
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Journal Article |
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Year |
2022 |
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IEEE Access journal |
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Vol. 10 |
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pp. 68281-68290 |
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yes |
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Call Number |
cidis @ cidis @ |
Serial |
183 |
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Author |
Xavier Soria; Edgar Riba; Angel D. Sappa |
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Title |
Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection |
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Conference Article |
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Year |
2020 |
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2020 IEEE Winter Conference on Applications of Computer Vision (WACV) |
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9093290 |
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1912-1921 |
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This paper proposes a Deep Learning based edge de- tector, which is inspired on both HED (Holistically-Nested Edge Detection) and Xception networks. The proposed ap- proach generates thin edge-maps that are plausible for hu- man eyes; it can be used in any edge detection task without previous training or fine tuning process. As a second contri- bution, 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 im- provements with the proposed method when F-measure of ODS and OIS are considered. |
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978-172816553-0 |
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no |
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Call Number |
cidis @ cidis @ |
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126 |
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