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Author Xavier Soria, Angel Sappa, Patricio Humanante, Arash Akbarinia pdf  url
doi  openurl
  Title (up) 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  
  Area Expedition Conference  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial 216  
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Author Xavier Soria; Edgar Riba; Angel D. Sappa pdf  isbn
openurl 
  Title (up) 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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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 126  
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Author Xavier Soria; Angel D. Sappa openurl 
  Title (up) 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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  Notes Approved no  
  Call Number gtsi @ user @ Serial 95  
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Author Xavier Soria , Gonzalo Pomboza-Junez & Angel Sappa. url  openurl
  Title (up) 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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  Notes Approved yes  
  Call Number cidis @ cidis @ Serial 183  
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Author Xavier Soria; Angel D. Sappa; Arash Akbarinia pdf  openurl
  Title (up) 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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  Notes Approved no  
  Call Number gtsi @ user @ Serial 72  
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Author Xavier Soria, Yachuan Li, Mohammad Rouhani & Angel D. Sappa pdf  openurl
  Title (up) Tiny and Efficient Model for the Edge Detection Generalization Type Conference Article
  Year 2023 Publication Proceedings – 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW 2023) Paris 2-6 October 2023 Abbreviated Journal  
  Volume Issue Pages 1356 - 1365  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 229  
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Author Xavier Soria; Angel D. Sappa; Riad Hammoud pdf  openurl
  Title (up) 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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  Notes Approved no  
  Call Number gtsi @ user @ Serial 96  
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