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Author (up) Xavier Soria , Gonzalo Pomboza-Junez & Angel Sappa.
Title LDC: Lightweight Dense CNN for Edge Detection. Type Journal Article
Year 2022 Publication IEEE Access journal Abbreviated Journal
Volume Vol. 10 Issue Pages pp. 68281-68290
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Call Number cidis @ cidis @ Serial 183
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Author (up) Xavier Soria, Angel Sappa, Patricio Humanante, Arash Akbarinia
Title Dense extreme inception network for edge detection. Type Journal Article
Year 2023 Publication Pattern Recognition Abbreviated Journal
Volume Vol. 139 Issue Pages
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ISSN 00313203 ISBN Medium
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Notes Approved no
Call Number cidis @ cidis @ Serial 216
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Author (up) Xavier Soria, Yachuan Li, Mohammad Rouhani & Angel D. Sappa
Title Tiny and Efficient Model for the Edge Detection Generalization Type Conference Article
Year 2023 Publication Workshop on Resource Efficient Deep Learning for Computer Vision – International Conference on Computer Vision ICCV 2023 Abbreviated Journal
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Call Number cidis @ cidis @ Serial 229
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Author (up) Xavier Soria; Angel D. Sappa
Title Improving Edge Detection in RGB Images by Adding NIR Channel. Type Conference Article
Year 2018 Publication 14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018) Abbreviated Journal
Volume Issue Pages 266-273
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Call Number gtsi @ user @ Serial 95
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Author (up) Xavier Soria; Angel D. Sappa; Arash Akbarinia
Title Multispectral Single-Sensor RGB-NIR Imaging: New Challenges an Oppotunities Type Conference Article
Year 2017 Publication The 7th International Conference on Image Processing Theory, Tools and Application Abbreviated Journal
Volume Issue Pages 1-6
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Call Number gtsi @ user @ Serial 72
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Author (up) Xavier Soria; Angel D. Sappa; Riad Hammoud
Title Wide-Band Color Imagery Restoration for RGB-NIR Single Sensor Image. Sensors 2018 ,2059. Type Journal Article
Year 2018 Publication Abbreviated Journal
Volume Vol. 18 Issue Issue 7 Pages
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Abstract Multi-spectral RGB-NIR sensors have become ubiquitous in recent years. These sensors allow the visible and near-infrared spectral bands of a given scene to be captured at the same time. With such cameras, the acquired imagery has a compromised RGB color representation due to near-infrared bands (700–1100 nm) cross-talking with the visible bands (400–700 nm). This paper proposes two deep learning-based architectures to recover the full RGB color images, thus removing the NIR information from the visible bands. The proposed approaches directly restore the high-resolution RGB image by means of convolutional neural networks. They are evaluated with several outdoor images; both architectures reach a similar performance when evaluated in different scenarios and using different similarity metrics. Both of them improve the state of the art approaches.
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Call Number gtsi @ user @ Serial 96
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Author (up) Xavier Soria; Edgar Riba; Angel D. Sappa
Title Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection Type Conference Article
Year 2020 Publication 2020 IEEE Winter Conference on Applications of Computer Vision (WACV) Abbreviated Journal
Volume Issue 9093290 Pages 1912-1921
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Abstract 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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ISSN ISBN 978-172816553-0 Medium
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Call Number cidis @ cidis @ Serial 126
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