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Author |
Boris Vintimilla, Jorge Vulgarin, Henry Velesaca |
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Title |
Deep Learning-based Human Height Estimation from a Stereo Vision System |
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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, 4-7 julio 2023 |
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979-835033337-4 |
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Call Number |
cidis @ cidis @ |
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215 |
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Author |
Steven Silva, Nervo Verdezoto, Dennys Paillacho, Samuel Millan-Norman & Juan David Hernandez |
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Title |
Online Social Robot Navigation in Indoor, Large and Crowded Environments. |
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Conference Article |
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Year |
2023 |
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IEEE International Conference on Robotics and Automation (ICRA 2023) Londres, 29 may 2023 – 2 jun 2023 |
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2023-May |
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9749 - 9756 |
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10504729 |
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979-835032365-8 |
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cidis @ cidis @ |
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206 |
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Author |
Wilton Agila, Jonathan Aviles, Abel Rubio, Leandro Gonzalez |
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Title |
Approximate Reasoning Techniques in the Control of States of Operation of the PEM Fuel Cell |
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Conference Article |
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2023 |
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11th International Conference On Smart Grid (ICSMARTGRID 2023) Paris, 4-7 junio 2023 |
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979-835031066-5 |
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cidis @ cidis @ |
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208 |
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Author |
Abel Rubio, Wilton Agila, Leandro Gonzalez, Maria Ramirez, Herman Pineda |
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Title |
A Critical Analysis of the Impact of the Pandemic on Sustainable Energy Scenarios |
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2023 |
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11th International Conference On Smart Grid (ICSMARTGRID 2023) Paris, 4-7 junio 2023 |
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979-835031066-5 |
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cidis @ cidis @ |
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209 |
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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 Image Challenge: Translation from Synthetic Aperture Radar to Electro-Optical Domain Results – PBVS 2023 |
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Conference Article |
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Year |
2023 |
Publication |
19th IEEE Workshop on Perception Beyond the Visible Spectrum de la Conferencia Computer Vision & Pattern Recognition (CVPR 2023) Vancouver, 18-28 junio 2023 |
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2023-June |
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515 - 523 |
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21607508 |
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979-835030249-3 |
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no |
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cidis @ cidis @ |
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211 |
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Author |
Rafael E. Rivadeneira, Angel D. Sappa, Boris X. Vintimilla, Chenyang Wang, Junjun Jiang, Xianming Liu, Zhiwei Zhong, Dai Bin, Li Ruodi, Li Shengye |
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Title |
Thermal Image Super-Resolution Challenge Results – PBVS 2023 |
Type |
Conference Article |
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Year |
2023 |
Publication |
19th IEEE Workshop on Perception Beyond the Visible Spectrum de la Conferencia Computer Vision & Pattern Recognition (CVPR 2023) Vancouver, 18-28 junio 2023 |
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2023-June |
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470 - 478 |
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21607508 |
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979-835030249-3 |
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no |
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Call Number |
cidis @ cidis @ |
Serial |
210 |
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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 |
Type |
Conference Article |
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Year |
2023 |
Publication |
19th IEEE Workshop on Perception Beyond the Visible Spectrum de la Conferencia Computer Vision & Pattern Recognition (CVPR 2023) Vancouver, 18-28 junio 2023 |
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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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no |
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Call Number |
cidis @ cidis @ |
Serial |
212 |
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Author |
Henry O. Velesaca, Juan Antonio Holgado-Terriza, Doménica Carrasco, José Miguel Gutiérrez Guerrero, Tonny Toscano, Darío Carpio & Angel Sappa |
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Title |
Anomaly Detection in Industrial Production Products using OPC-UA and Deep Learning |
Type |
Conference Article |
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Year |
2024 |
Publication |
13th International Conference on Data Science, Technology and Applications, DATA 2024 Dijon 9-11 July 2024 |
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505 - 512 |
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978-989758707-8 |
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no |
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Call Number |
cidis @ cidis @ |
Serial |
232 |
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Author |
Jorge L. Charco; Angel D. Sappa; Boris X. Vintimilla; Henry O. Velesaca |
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Title |
Transfer Learning from Synthetic Data in the Camera Pose Estimation Problem |
Type |
Conference Article |
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Year |
2020 |
Publication |
The 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020); Valletta, Malta; 27-29 Febrero 2020 |
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4 |
Issue |
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Pages |
498-505 |
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Keywords |
Relative Camera Pose Estimation, Siamese Architecture, Synthetic Data, Deep Learning, Multi-View Environments, Extrinsic Camera Parameters. |
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Abstract |
This paper presents a novel Siamese network architecture, as a variant of Resnet-50, to estimate the relative camera pose on multi-view environments. In order to improve the performance of the proposed model
a transfer learning strategy, based on synthetic images obtained from a virtual-world, is considered. The
transfer learning consist of first training the network using pairs of images from the virtual-world scenario
considering different conditions (i.e., weather, illumination, objects, buildings, etc.); then, the learned weight
of the network are transferred to the real case, where images from real-world scenarios are considered. Experimental results and comparisons with the state of the art show both, improvements on the relative pose
estimation accuracy using the proposed model, as well as further improvements when the transfer learning
strategy (synthetic-world data – transfer learning – real-world data) is considered to tackle the limitation on
the training due to the reduced number of pairs of real-images on most of the public data sets. |
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ISBN |
978-989758402-2 |
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no |
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Call Number |
gtsi @ user @ |
Serial |
120 |
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Author |
Rafael E. Rivadeneira; Angel D. Sappa; Boris X. Vintimilla |
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Title |
Thermal Image Super-Resolution: a Novel Architecture and Dataset |
Type |
Conference Article |
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Year |
2020 |
Publication |
The 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020); Valletta, Malta; 27-29 Febrero 2020 |
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4 |
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Pages |
111-119 |
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Keywords |
Thermal images, Far Infrared, Dataset, Super-Resolution. |
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Abstract |
This paper proposes a novel CycleGAN architecture for thermal image super-resolution, together with a large
dataset consisting of thermal images at different resolutions. The dataset has been acquired using three thermal
cameras at different resolutions, which acquire images from the same scenario at the same time. The thermal
cameras are mounted in rig trying to minimize the baseline distance to make easier the registration problem.
The proposed architecture is based on ResNet6 as a Generator and PatchGAN as Discriminator. The novelty
on the proposed unsupervised super-resolution training (CycleGAN) is possible due to the existence of aforementioned thermal images—images of the same scenario with different resolutions. The proposed approach
is evaluated in the dataset and compared with classical bicubic interpolation. The dataset and the network are
available. |
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978-989758402-2 |
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no |
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Call Number |
gtsi @ user @ |
Serial |
121 |
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