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Author (up) Xavier Soria; Angel D. Sappa openurl 
  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 pdf  openurl
  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 pdf  openurl
  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 pdf  isbn
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  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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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 126  
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