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Author |
Rato D., Oliveira M., Santos V., Sappa A. & Raducanu B. |
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Title |
Multi-View 2D to 3D Lifting Video-Based Optimization: A Robust Approach for Human Pose Estimation with Occluded Joint Prediction |
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2024 |
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IEEE Int. Conf. on Intelligent Robots and Systems (IROS), Abu Dhabi, October 14-18, 2024 |
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cidis @ cidis @ |
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255 |
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Author |
Patricia Suarez & Angel D. Sappa |

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Title |
Synthetic Thermal Image Generation from Multi-Cue Input Data |
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2025 |
Publication |
20th International Conference on Computer Vision Theory and Applications VISIGRAPP 2025 Porto 26-28 Febrero 2025 |
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Vol. 3 |
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275 - 282 |
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cidis @ cidis @ |
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265 |
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Author |
Gisel Bastidas G., Patricio Moreno V., Boris Vintimilla & Angel D. Sappa |

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Title |
Application-Guided Image Fusion: A Path to Improve Results in High-Level Vision Tasks |
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2025 |
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20th International Conference on Computer Vision Theory and Applications VISIGRAPP 2025 Porto 26-28 Febrero 2025 |
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Vol. 3 |
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178 - 187 |
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cidis @ cidis @ |
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266 |
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Author |
Henry O. Velesaca, Angel D. Sappa & Juan A. Holgado |
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Title |
A Case Study of Anomaly Detection in Tinplate Lids: Supervised vs Unsupervised approaches |
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2025 |
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11th International Conference on Automation, Robotics, and Applications (ICARA 2025) |
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cidis @ cidis @ |
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267 |
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Author |
Henry O. Velesaca & Angel D. Sappa |
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Title |
Seeing the Unseen: AI-Powered Camouflaged Pest Detection |
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Journal Article |
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2025 |
Publication |
9th International Conference on Machine Vision and Information Technology (CMVIT 2025) |
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no |
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cidis @ cidis @ |
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268 |
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Author |
Constantine Macías A., Toala Paz A., Realpe Miguel, Suárez Moncada Jenifer, Páez Rosas Diego & Jarrín Enrique Peláez |
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Title |
Leveraging Deep Learning Techniques for Marine and Coastal Wildlife Using Instance Segmentation: A Study on Galápagos Sea Lions |
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Journal Article |
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2024 |
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In 8th Ecuador Technical Chapters Meeting (ETCM 2024) Cuenca, October 15 – October 18, 2024 |
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no |
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Call Number |
cidis @ cidis @ |
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269 |
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Author |
Leo Thomas Ramos & Angel D. Sappa |

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Title |
Leveraging U-Net and selective feature extraction for land cover classification using remote sensing imagery |
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Journal Article |
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Year |
2025 |
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Scientific Reports |
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Vol. 15 |
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no |
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cidis @ cidis @ |
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270 |
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Author |
Henry O. Velesaca and Juan A. Holgado-Terriza |

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Title |
OPC-UA in artificial intelligence: a systematic review of the integration of data mining and NLP in industrial processes |
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Year |
2025 |
Publication |
Manufacturing Review |
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Vol. 12 |
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no |
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Call Number |
cidis @ cidis @ |
Serial |
273 |
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Author |
Nathan Inkawhich, Claire Thorp, Justice Wheelwright, Oliver Nina, Dylan Bowald, Angel Sappa, Erik Blasch |
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Title |
4th Multi-modal Aerial View Image Challenge: SAR CLASSIFICATION – PBVS 2025 |
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Journal Article |
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Year |
2025 |
Publication |
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops CVPRW 2025 |
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Call Number |
cidis @ cidis @ |
Serial |
276 |
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Author |
Cristhian A. Aguilera, Cristhian Aguilera, Cristóbal A. Navarro, & Angel D. Sappa |

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Title |
Fast CNN Stereo Depth Estimation through Embedded GPU Devices |
Type |
Journal Article |
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Year |
2020 |
Publication |
Sensors 2020 |
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Volume |
Vol. 2020-June |
Issue  |
11 |
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pp. 1-13 |
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Keywords |
stereo matching; deep learning; embedded GPU |
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Abstract |
Current CNN-based stereo depth estimation models can barely run under real-time
constraints on embedded graphic processing unit (GPU) devices. Moreover, state-of-the-art
evaluations usually do not consider model optimization techniques, being that it is unknown what is
the current potential on embedded GPU devices. In this work, we evaluate two state-of-the-art models
on three different embedded GPU devices, with and without optimization methods, presenting
performance results that illustrate the actual capabilities of embedded GPU devices for stereo depth
estimation. More importantly, based on our evaluation, we propose the use of a U-Net like architecture
for postprocessing the cost-volume, instead of a typical sequence of 3D convolutions, drastically
augmenting the runtime speed of current models. In our experiments, we achieve real-time inference
speed, in the range of 5–32 ms, for 1216 368 input stereo images on the Jetson TX2, Jetson Xavier,
and Jetson Nano embedded devices. |
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English |
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English |
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14248220 |
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no |
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cidis @ cidis @ |
Serial |
132 |
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