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| Author | Nathan Inkawhich, Claire Thorp, Justice Wheelwright, Oliver Nina, Dylan Bowald, Angel Sappa, Erik Blasch | ||||
| Title | 4th Multi-modal Aerial View Image Challenge: SAR CLASSIFICATION – PBVS 2025 | Type | Journal Article | ||
| Year | 2025 | Publication | IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops CVPRW 2025 | Abbreviated Journal | |
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| Call Number | cidis @ cidis @ | Serial | 276 | ||
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| Author | Dylan Bowald, Justice Wheelwright, Oliver Nina, Angel Sappa, Riad Hammoud, Erik Blasch, Nathan Inkawhich | ||||
| Title | 3th Multi-modal Aerial View Image Challenge: Sensor Domain Translation – PBVS 2025 | Type | Conference Article | ||
| Year | 2025 | Publication | IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops CVPRW 2025 | Abbreviated Journal | |
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| Call Number | cidis @ cidis @ | Serial | 277 | ||
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| Author | Patricia Suarez & Angel D. Sappa | ||||
| Title | Lightweight Architecture for Fruit Quality Estimation in the Infrared Domain | Type | Conference Article | ||
| Year | 2025 | Publication | 5th International Conference on Innovations in Computational Intelligence and Computer Vision ICICV 2025 | Abbreviated Journal | |
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| Call Number | cidis @ cidis @ | Serial | 278 | ||
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| Author | Henry O. Velesaca Hector Villegas, and Angel D. Sappa | ||||
| Title | Exploring Camouflaged Object Detection Techniques for Invasive Vegetation Monitoring | Type | Conference Article | ||
| Year | 2025 | Publication | 14th International Conference on Data Science, Technology and Applications DATA 2025 | Abbreviated Journal | |
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| Call Number | cidis @ cidis @ | Serial | 279 | ||
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| Author | Kevin E. Munoz, Loberlly N. Salazar, Steven S. Araujo, and Boris X. Vintimilla | ||||
| Title | Stereo Vision Techniques: A Comparative Study of Traditional and Machine Learning-Based Approaches | Type | Conference Article | ||
| Year | 2025 | Publication | 5th International Conference on Computer Vision and Robotics CVR 2025 | Abbreviated Journal | |
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| Call Number | cidis @ cidis @ | Serial | 280 | ||
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| Author | Kevin E. Muñoz Loberlly N. Salazar Steven S. Araujo Boris X. Vintimilla | ||||
| Title | Detecting and Characterizing Human Interactions to EnhanceHuman-Robot Engagement | Type | Conference Article | ||
| Year | 2025 | Publication | 3rd International Conference on Robotics, Control and Vision Engineering RCVE 2025 | Abbreviated Journal | |
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| Call Number | cidis @ cidis @ | Serial | 281 | ||
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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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| Series Volume | Series Issue | Edition | |||
| 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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| Author | Xavier Soria; Edgar Riba; Angel D. Sappa | ||||
| Title | Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection | Type | Conference Article | ||
| Year | 2020 | Publication | 2020 IEEE Winter Conference on Applications of Computer Vision (WACV) | Abbreviated Journal | |
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9093290 | Pages | 1912-1921 | |
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| Abstract | This paper proposes a Deep Learning based edge de- tector, which is inspired on both HED (Holistically-Nested Edge Detection) and Xception networks. The proposed ap- proach generates thin edge-maps that are plausible for hu- man eyes; it can be used in any edge detection task without previous training or fine tuning process. As a second contri- bution, a large dataset with carefully annotated edges, has been generated. This dataset has been used for training the proposed approach as well the state-of-the-art algorithms for comparisons. Quantitative and qualitative evaluations have been performed on different benchmarks showing im- provements with the proposed method when F-measure of ODS and OIS are considered. | ||||
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| ISSN | ISBN | 978-172816553-0 | Medium | ||
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 126 | ||
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