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| Author | Gisel Bastidas G., Patricio Moreno V., Boris Vintimilla & Angel D. Sappa | ||||
| 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 | ||||
| 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 | ||||
| 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 | ||||
| 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 | ||||
| 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 | Leo Thomas Ramos & Angel D. Sappa | ||||
| Title | Dual-branch ConvNeXt-based Network with Attentional Fusion Decoding for Land Cover Classification Using Multispectral Imagery | Type | Conference Article | ||
| Year | 2025 | Publication | IEEE SoutheastCon 2025 | Abbreviated Journal | |
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| Call Number | cidis @ cidis @ | Serial | 271 | ||
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| Author | Leo Thomas Ramos & Angel D. Sappa | ||||
| Title | Enhanced Aerial Scene Classification Through ConvNeXt Architectures and Channel Attention | Type | Conference Article | ||
| Year | 2025 | Publication | 10th International Congress on Information and Communication Technology | Abbreviated Journal | |
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| Call Number | cidis @ cidis @ | Serial | 272 | ||
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| Author | Cristhian A. Aguilera, Cristhian Aguilera, Cristóbal A. Navarro, & Angel D. Sappa | ||||
| 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 |
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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| Language | English | Summary Language | English | Original Title | |
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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 | ||||
| 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 |
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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| ISSN | ISBN | 14248220 | Medium | ||
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 133 | ||
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| Author | Morocho-Cayamcela, M.E. & W. Lim | ||||
| 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 |
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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| Call Number | cidis @ cidis @ | Serial | 139 | ||
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