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Author Rato D., Oliveira M., Santos V., Sappa A. & Raducanu B. openurl 
  Title Multi-View 2D to 3D Lifting Video-Based Optimization: A Robust Approach for Human Pose Estimation with Occluded Joint Prediction Type Journal Article
  Year 2024 Publication IEEE Int. Conf. on Intelligent Robots and Systems (IROS), Abu Dhabi, October 14-18, 2024 Abbreviated Journal  
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  Call Number cidis @ cidis @ Serial 255  
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Author Patricia Suarez & Angel D. Sappa openurl 
  Title Synthetic Thermal Image Generation from Multi-Cue Input Data Type Journal Article
  Year 2025 Publication 20th International Conference on Computer Vision Theory and Applications (VISAPP 2025) Abbreviated Journal  
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  Call Number cidis @ cidis @ Serial 265  
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Author Gisel Bastidas G., Patricio Moreno V., Boris Vintimilla & Angel D. Sappa openurl 
  Title Application-Guided Image Fusion: A Path to Improve Results in High-Level Vision Tasks Type Journal Article
  Year 2025 Publication 20th International Conference on Computer Vision Theory and Applications VISAPP 2025 Abbreviated Journal  
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  Call Number cidis @ cidis @ Serial 266  
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Author Henry O. Velesaca, Angel D. Sappa & Juan A. Holgado openurl 
  Title A Case Study of Anomaly Detection in Tinplate Lids: Supervised vs Unsupervised approaches Type Journal Article
  Year 2025 Publication 11th International Conference on Automation, Robotics, and Applications (ICARA 2025) Abbreviated Journal  
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  Call Number cidis @ cidis @ Serial 267  
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Author Henry O. Velesaca & Angel D. Sappa openurl 
  Title Seeing the Unseen: AI-Powered Camouflaged Pest Detection Type Journal Article
  Year 2025 Publication 9th International Conference on Machine Vision and Information Technology (CMVIT 2025) Abbreviated Journal  
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  Call Number cidis @ cidis @ Serial 268  
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Author Constantine Macías A., Toala Paz A., Realpe Miguel, Suárez Moncada Jenifer, Páez Rosas Diego & Jarrín Enrique Peláez openurl 
  Title Leveraging Deep Learning Techniques for Marine and Coastal Wildlife Using Instance Segmentation: A Study on Galápagos Sea Lions Type Journal Article
  Year 2024 Publication In 8th Ecuador Technical Chapters Meeting (ETCM 2024) Cuenca, October 15 – October 18, 2024 Abbreviated Journal  
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  Call Number cidis @ cidis @ Serial 269  
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Author Leo Thomas Ramos & Angel D. Sappa pdf  openurl
  Title Leveraging U-Net and selective feature extraction for land cover classification using remote sensing imagery Type Journal Article
  Year 2025 Publication Scientific Reports Abbreviated Journal  
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  Call Number cidis @ cidis @ Serial 270  
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Author Cristhian A. Aguilera, Cristhian Aguilera, Cristóbal A. Navarro, & Angel D. Sappa pdf  openurl
  Title Fast CNN Stereo Depth Estimation through Embedded GPU Devices Type Journal Article
  Year 2020 Publication Sensors 2020 Abbreviated Journal  
  Volume Vol. 2020-June Issue (up) 11 Pages pp. 1-13  
  Keywords stereo matching; deep learning; embedded GPU  
  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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  ISSN 14248220 ISBN Medium  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 132  
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Author Ángel Morera, Ángel Sánchez, A. Belén Moreno, Angel D. Sappa, & José F. Vélez pdf  isbn
openurl 
  Title SSD vs. YOLO for Detection of Outdoor Urban Advertising Panels under Multiple Variabilities. Type Journal Article
  Year 2020 Publication Abbreviated Journal In Sensors  
  Volume Vol. 2020-August Issue (up) 16 Pages pp. 1-23  
  Keywords object detection; urban outdoor panels; one-stage detectors; Single Shot MultiBox Detector (SSD); You Only Look Once (YOLO); detection metrics; object and scene imaging variabilities  
  Abstract This work compares Single Shot MultiBox Detector (SSD) and You Only Look Once (YOLO)

deep neural networks for the outdoor advertisement panel detection problem by handling multiple

and combined variabilities in the scenes. Publicity panel detection in images o ers important

advantages both in the real world as well as in the virtual one. For example, applications like Google

Street View can be used for Internet publicity and when detecting these ads panels in images, it could

be possible to replace the publicity appearing inside the panels by another from a funding company.

In our experiments, both SSD and YOLO detectors have produced acceptable results under variable

sizes of panels, illumination conditions, viewing perspectives, partial occlusion of panels, complex

background and multiple panels in scenes. Due to the diculty of finding annotated images for the

considered problem, we created our own dataset for conducting the experiments. The major strength

of the SSD model was the almost elimination of False Positive (FP) cases, situation that is preferable

when the publicity contained inside the panel is analyzed after detecting them. On the other side,

YOLO produced better panel localization results detecting a higher number of True Positive (TP)

panels with a higher accuracy. Finally, a comparison of the two analyzed object detection models

with di erent types of semantic segmentation networks and using the same evaluation metrics is

also included.
 
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  ISSN ISBN 14248220 Medium  
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  Call Number cidis @ cidis @ Serial 133  
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Author Morocho-Cayamcela, M.E. & W. Lim pdf  openurl
  Title Lateral confinement of high-impedance surface-waves through reinforcement learning Type Journal Article
  Year 2020 Publication Electronics Letters Abbreviated Journal  
  Volume Vol. 56 Issue (up) 23, 12 November 2020 Pages pp. 1262-1264  
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  Abstract The authors present a model-free policy-based reinforcement learning

model that introduces perturbations on the pattern of a metasurface.

The objective is to learn a policy that changes the size of the

patches, and therefore the impedance in the sides of an artificially structured

material. The proposed iterative model assigns the highest reward

when the patch sizes allow the transmission along a constrained path

and penalties when the patch sizes make the surface wave radiate to

the sides of the metamaterial. After convergence, the proposed

model learns an optimal patch pattern that achieves lateral confinement

along the metasurface. Simulation results show that the proposed

learned-pattern can effectively guide the electromagnetic wave

through a metasurface, maintaining its instantaneous eigenstate when

the homogeneity is perturbed. Moreover, the pattern learned to

prevent reflections by changing the patch sizes adiabatically. The

reflection coefficient S1, 2 shows that most of the power gets transferred

from the source to the destination with the proposed design.
 
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 139  
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