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Author |
Spencer Low, Oliver Nina, Angel D. Sappa, Erik Blasch, Nathan Inkawhich |
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Title |
Multi-modal Aerial View Object Classification Challenge Results – PBVS 2023 |
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Conference Article |
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Year |
2023 |
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19th IEEE Workshop on Perception Beyond the Visible Spectrum de la Conferencia Computer Vision & Pattern Recognition CVPR 2023, junio 18-28 |
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2023-June |
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412 - 421 |
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21607508 |
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979-835030249-3 |
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cidis @ cidis @ |
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212 |
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Author |
Gisel Bastidas-Guacho, Patricio Moreno-Vallejo, Boris Vintimilla, Angel D. Sappa |
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Title |
Application on the Loop of Multimodal Image Fusion: Trends on Deep-Learning Based Approaches |
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Conference Article |
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2023 |
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IEEE 13th International Conference on Pattern Recognition Systems ICPRS 2023, julio 4-7 |
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979-835033337-4 |
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cidis @ cidis @ |
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213 |
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Author |
Rafael E. Rivadeneira, Henry O. Velesaca, Angel D. Sappa |
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Title |
Object Detection in Very Low-Resolution Thermal Images through a Guided-Based Super-Resolution Approach |
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Conference Article |
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Year |
2023 |
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17th International Conference On Signal Image Technology & Internet Based System |
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cidis @ cidis @ |
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224 |
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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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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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cidis @ cidis @ |
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229 |
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Author |
Rafael E. Rivadeneira, Angel D. Sappa, Chenyang Wang, Junjun Jiang, Zhiwei Zhong, Peilin Chen & Shiqi Wang |
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Title |
Thermal Image Super Resolution Challenge Results – PBVS 2024 |
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Conference Article |
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Year |
2024 |
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Accepted in 20th IEEE Workshop on Perception Beyond the Visible Spectrum of the 2024 Conference on Computer Vision and Pattern Recognition |
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cidis @ cidis @ |
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233 |
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Author |
Angel D. Sappa, Spencer Low, Oliver Nina, Erik Blasch, Dylan Bowald & Nathan Inkawhich |
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Title |
Multi-modal Aerial View Image Challenge: SAR Classification |
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Conference Article |
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Year |
2024 |
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Accepted in 20th IEEE Workshop on Perception Beyond the Visible Spectrum of the 2024 Conference on Computer Vision and Pattern Recognition |
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cidis @ cidis @ |
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234 |
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Angel D. Sappa, Spencer Low, Oliver Nina, Erik Blasch, Dylan Bowald & Nathan Inkawhich |
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Title |
Multi-modal Aerial View Image Challenge: Sensor Domain Translation |
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Conference Article |
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Year |
2024 |
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Accepted in 20th IEEE Workshop on Perception Beyond the Visible Spectrum of the 2024 Conference on Computer Vision and Pattern Recognition |
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cidis @ cidis @ |
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235 |
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Author |
Patricia Suarez & Angel D. Sappa |
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Title |
Haze-Free Imaging through Haze-Aware Transformer Adaptations |
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Conference Article |
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Year |
2024 |
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In Fourth International Conference on Innovations in Computational Intelligence and Computer Vision (ICICV 2024) |
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cidis @ cidis @ |
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236 |
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Author |
Patricia Suarez, Angel D. Sappa |
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Title |
A Generative Model for Guided Thermal Image Super-Resolution |
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Year |
2024 |
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In 19th International Conference on Computer Vision Theory and Applications VISAPP 2024 |
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Call Number |
cidis @ cidis @ |
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240 |
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Author |
Cristhian A. Aguilera; Cristhian Aguilera; Angel D. Sappa |
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Title |
Melamine faced panels defect classification beyond the visible spectrum. |
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Journal Article |
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Year |
2018 |
Publication |
In Sensors 2018 |
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Vol. 11 |
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Issue 11 |
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Abstract |
In this work, we explore the use of images from different spectral bands to classify defects in melamine faced panels, which could appear through the production process. Through experimental evaluation, we evaluate the use of images from the visible (VS), near-infrared (NIR), and long wavelength infrared (LWIR), to classify the defects using a feature descriptor learning approach together with a support vector machine classifier. Two descriptors were evaluated, Extended Local Binary Patterns (E-LBP) and SURF using a Bag of Words (BoW) representation. The evaluation was carried on with an image set obtained during this work, which contained five different defect categories that currently occurs in the industry. Results show that using images from beyond
the visual spectrum helps to improve classification performance in contrast with a single visible spectrum solution. |
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gtsi @ user @ |
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89 |
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