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Author |
Armin Mehri; Parichehr Behjati; Angel Domingo Sappa |
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Title |
TnTViT-G: Transformer in Transformer Network for Guidance Super Resolution. |
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2023 |
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IEEE Access |
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Vol. 11 |
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pp. 11529-11540 |
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21693536 |
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no |
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Call Number |
cidis @ cidis @ |
Serial |
207 |
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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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Year |
2023 |
Publication |
Pattern Recognition |
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Vol. 139 |
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00313203 |
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no |
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cidis @ cidis @ |
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216 |
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Author |
Abel Rubio, Wilton Agila, Leandro González & Jonathan Aviles-Cedeno |
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Title |
Distributed Intelligence in Autonomous PEM Fuel Cell Control. |
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Journal Article |
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Year |
2023 |
Publication |
Energies 2023 |
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Volume |
Vol. 16 |
Issue |
Issue 12 |
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19961073 |
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no |
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Call Number |
cidis @ cidis @ |
Serial |
217 |
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Author |
Patricia Súarez, Henry Velesaca, Dario Carpio & Angel Sappa |
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Title |
Corn Kernel Classification From Few Training Samples |
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Journal Article |
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Year |
2023 |
Publication |
In journal Artificial Intelligence in Agriculture |
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Volume |
Vol. 9 |
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Pages |
pp. 89-99 |
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25897217 |
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Approved |
no |
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Call Number |
cidis @ cidis @ |
Serial |
223 |
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Author |
Armin Mehri, Parichehr Behjati, Dario Carpio, and Angel D. Sappa |
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Title |
SRFormer: Efficient Yet Powerful Transformer Network For Single Image Super Resolution |
Type |
Journal Article |
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Year |
2023 |
Publication |
IEEE access |
Abbreviated Journal |
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Volume |
Vol. 11 |
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Pages |
121457 - 121469 |
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21693536 |
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no |
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Call Number |
cidis @ cidis @ |
Serial |
227 |
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Author |
Rafael E. Rivadeneira, Angel Domingo Sappa, Vintimilla B. X. and Hammoud R. |
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Title |
A Novel Domain Transfer-Based Approach for Unsupervised Thermal Image Super- Resolution. |
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Journal Article |
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Year |
2022 |
Publication |
Sensors |
Abbreviated Journal |
Sensors |
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Volume |
Vol. 22 |
Issue |
Issue 6 |
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no |
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Call Number |
cidis @ cidis @ |
Serial |
170 |
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Author |
Daniela Rato, Miguel Oliviera, Victor Santos, Manuel Gomes & Angel Sappa |
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Title |
A Sensor-to-Pattern Calibration Framework for Multi-Modal Industrial Collaborative Cells. |
Type |
Journal Article |
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Year |
2022 |
Publication |
Journal of Manufacturing Systems |
Abbreviated Journal |
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Volume |
Vol. 64 |
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Pages |
pp. 497-507 |
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Approved |
yes |
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Call Number |
cidis @ cidis @ |
Serial |
184 |
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Author |
Xavier Soria , Gonzalo Pomboza-Junez & Angel Sappa. |
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Title |
LDC: Lightweight Dense CNN for Edge Detection. |
Type |
Journal Article |
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Year |
2022 |
Publication |
IEEE Access journal |
Abbreviated Journal |
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Volume |
Vol. 10 |
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Pages |
pp. 68281-68290 |
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Approved |
yes |
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Call Number |
cidis @ cidis @ |
Serial |
183 |
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Author |
Ulises Gildardo Quiroz Antúnez, Alejandro Ismael Monterroso Rivas, María Fernanda Calderón Vega, Adán Guillermo Ramírez García |
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Title |
APTITUDE OF COFFEE (COFFEA ARABICA L.) AND CACAO (THEOBROMA CACAO L.) CROPS CONSIDERING CLIMATE CHANGE |
Type |
Journal Article |
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Year |
2022 |
Publication |
Granja |
Abbreviated Journal |
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Volume |
Vol. 36 |
Issue |
Issue 2 |
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Approved |
no |
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Call Number |
cidis @ cidis @ |
Serial |
200 |
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Author |
Charco, J.L., Sappa, A.D., Vintimilla, B.X., Velesaca, H.O. |
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Title |
Camera pose estimation in multi-view environments:from virtual scenarios to the real world |
Type |
Journal Article |
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Year |
2021 |
Publication |
In Image and Vision Computing Journal. (Article number 104182) |
Abbreviated Journal |
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Volume |
Vol. 110 |
Issue |
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Pages |
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Keywords |
Relative camera pose estimation, Domain adaptation, Siamese architecture, Synthetic data, Multi-view environments |
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Abstract |
This paper presents a domain adaptation strategy to efficiently train network architectures for estimating the relative camera pose in multi-view scenarios. The network architectures are fed by a pair of simultaneously acquired
images, hence in order to improve the accuracy of the solutions, and due to the lack of large datasets with pairs of
overlapped images, a domain adaptation strategy is proposed. The domain adaptation strategy consists on transferring the knowledge learned from synthetic images to real-world scenarios. For this, the networks are firstly
trained using pairs of synthetic images, which are captured at the same time by a pair of cameras in a virtual environment; and then, the learned weights of the networks are transferred to the real-world case, where the networks are retrained with a few real images. Different virtual 3D scenarios are generated to evaluate the
relationship between the accuracy on the result and the similarity between virtual and real scenarios—similarity
on both geometry of the objects contained in the scene as well as relative pose between camera and objects in the
scene. Experimental results and comparisons are provided showing that the accuracy of all the evaluated networks for estimating the camera pose improves when the proposed domain adaptation strategy is used,
highlighting the importance on the similarity between virtual-real scenarios. |
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English |
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English |
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no |
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Call Number |
cidis @ cidis @ |
Serial |
147 |
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