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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 VISAPP 2025 |
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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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2025 |
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9th International Conference on Machine Vision and Information Technology (CMVIT 2025) |
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cidis @ cidis @ |
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268 |
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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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2024 |
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In 8th Ecuador Technical Chapters Meeting (ETCM 2024) Cuenca, October 15 – October 18, 2024 |
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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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2025 |
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Scientific Reports |
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cidis @ cidis @ |
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270 |
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Author |
Leo Thomas Ramos & Angel D. Sappa |
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Title |
Dual-branch ConvNeXt-based Network with Attentional Fusion Decoding for Land Cover Classification Using Multispectral Imagery |
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2025 |
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IEEE SoutheastCon 2025 |
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Call Number |
cidis @ cidis @ |
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271 |
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Author |
Leo Thomas Ramos & Angel D. Sappa |
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Title |
Enhanced Aerial Scene Classification Through ConvNeXt Architectures and Channel Attention |
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Conference Article |
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2025 |
Publication |
10th International Congress on Information and Communication Technology |
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cidis @ cidis @ |
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272 |
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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 |
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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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14248220 |
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Call Number |
cidis @ cidis @ |
Serial |
132 |
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Author |
Ángel Morera, Ángel Sánchez, A. Belén Moreno, Angel D. Sappa, & José F. Vélez |
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Title |
SSD vs. YOLO for Detection of Outdoor Urban Advertising Panels under Multiple Variabilities. |
Type |
Journal Article |
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Year |
2020 |
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In Sensors |
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Volume |
Vol. 2020-August |
Issue |
16 |
Pages |
pp. 1-23 |
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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 |
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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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14248220 |
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Call Number |
cidis @ cidis @ |
Serial |
133 |
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Author |
Morocho-Cayamcela, M.E. & W. Lim |
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Title |
Lateral confinement of high-impedance surface-waves through reinforcement learning |
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Year |
2020 |
Publication |
Electronics Letters |
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Vol. 56 |
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23, 12 November 2020 |
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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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Call Number |
cidis @ cidis @ |
Serial |
139 |
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