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Author Patricia Suarez, Angel Sappa openurl 
  Title Depth-Conditioned Thermal-like Image Generation Type Conference Article
  Year 2024 Publication Accepted in 14th International Conference on Pattern Recognition Systems (ICPRS) Abbreviated Journal  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 243  
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Author Omar Coello, Moisés Coronel, Darío Carpio, Boris X. Vintimilla & Luis Chuquimarca openurl 
  Title Enhancing Apple’s Defect Classification: Insights from Visible Spectrum and Narrow Spectral Band Imaging Type Conference Article
  Year 2024 Publication Accepted in 14th International Conference on Pattern Recognition Systems (ICPRS) Abbreviated Journal  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 244  
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Author Luis Chuquimarca, Boris X. Vintimilla & Sergio Velastin openurl 
  Title Classifying Healthy and Defective Fruits with a Siamese Architecture and CNN Models Type Conference Article
  Year 2024 Publication Accepted in 14th International Conference on Pattern Recognition Systems (ICPRS) Abbreviated Journal  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 245  
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Author Jácome Galarza, Luis Roberto openurl 
  Title Estimation of Corn Crop Yield using Multimodal Deep Learning from Multispectral Images and Environmental Sensors Type Conference Article
  Year 2024 Publication In accepted in 19ª Conferência Ibérica de Sistemas e Tecnologias de Informação; CISTI'2024 Abbreviated Journal  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 246  
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Author Patricia L. Suarez, Dario Carpio, Angel D. Sappa pdf  openurl
  Title Enhancement of Guided Thermal Image Super-Resolution Approaches Type Journal
  Year 2024 Publication Neurocomputing Abbreviated Journal  
  Volume 573 Issue (up) Pages  
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  Area Expedition Conference Neurocomputing  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial 247  
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Author Henry O. Velesaca, Gisel Bastidas, Mohammad Rouhani, Angel D. Sappa pdf  doi
openurl 
  Title Multimodal image registration techniques: a comprehensive survey Type Journal Article
  Year 2024 Publication Multimedia Tools and Applications Abbreviated Journal  
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  Series Volume Series Issue Edition  
  ISSN 13807501 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial 248  
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Author Emmanuel F. Morán, Boris X. Vintimilla, Miguel A. Realpe url  doi
isbn  openurl
  Title Towards a Robust Solution for the Supermarket Shelf Audit Problem: Obsolete Price Tags in Shelves Type Conference Article
  Year 2024 Publication 26th Iberoamerican Congress on Pattern Recognition, CIARP 2023 Abbreviated Journal  
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  Series Volume Series Issue Edition  
  ISSN 03029743 ISBN 978-303149017-0 Medium  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 249  
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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.
 
  Address  
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  Language English Summary Language English Original Title  
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  Series Volume Series Issue Edition  
  ISSN 14248220 ISBN Medium  
  Area Expedition Conference  
  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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  Language English Summary Language English Original Title  
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  Series Volume Series Issue Edition  
  ISSN ISBN 14248220 Medium  
  Area Expedition Conference  
  Notes Approved no  
  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  
  Keywords  
  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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  Area Expedition Conference  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial 139  
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