| Records |
| Author |
Angel D. Sappa. |
| Title |
ICT Applications for Smart Cities |
Type |
Book Chapter |
| Year |
2022 |
Publication |
Intelligent Systems Reference Library |
Abbreviated Journal |
BOOK |
| Volume |
224 |
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Approved |
no |
| Call Number |
cidis @ cidis @ |
Serial |
198 |
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| Author |
Spencer Low, Oliver Nina, Angel D. Sappa, Erik Blasch, Nathan Inkawhich |
| Title |
Multi-modal Aerial View Image Challenge: Translation from Synthetic Aperture Radar to Electro-Optical Domain Results – PBVS 2023 |
Type |
Conference Article |
| Year |
2023 |
Publication |
19th IEEE Workshop on Perception Beyond the Visible Spectrum de la Conferencia Computer Vision & Pattern Recognition CVPR 2023, junio 18-28 |
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| Volume |
2023-June |
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Pages |
515 - 523 |
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| ISSN |
21607508 |
ISBN |
979-835030249-3 |
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no |
| Call Number |
cidis @ cidis @ |
Serial |
211 |
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| Author |
Rafael E. Rivadeneira, Angel D. Sappa, Boris X. Vintimilla, Chenyang Wang, Junjun Jiang, Xianming Liu, Zhiwei Zhong, Dai Bin, Li Ruodi, Li Shengye |
| Title |
Thermal Image Super-Resolution Challenge Results – PBVS 2023 |
Type |
Conference Article |
| Year |
2023 |
Publication |
19th IEEE Workshop on Perception Beyond the Visible Spectrum de la Conferencia Computer Vision & Pattern Recognition CVPR 2023, junio 18-28 |
Abbreviated Journal |
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2023-June |
Issue |
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Pages |
470 - 478 |
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21607508 |
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979-835030249-3 |
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no |
| Call Number |
cidis @ cidis @ |
Serial |
210 |
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| Author |
Spencer Low, Oliver Nina, Angel D. Sappa, Erik Blasch, Nathan Inkawhich |
| Title |
Multi-modal Aerial View Object Classification Challenge Results – PBVS 2023 |
Type |
Conference Article |
| Year |
2023 |
Publication |
19th IEEE Workshop on Perception Beyond the Visible Spectrum de la Conferencia Computer Vision & Pattern Recognition CVPR 2023, junio 18-28 |
Abbreviated Journal |
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| Volume |
2023-June |
Issue |
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Pages |
412 - 421 |
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21607508 |
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979-835030249-3 |
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no |
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cidis @ cidis @ |
Serial |
212 |
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| Author |
Gisel Bastidas-Guacho, Patricio Moreno-Vallejo, Boris Vintimilla, Angel D. Sappa |
| Title |
Application on the Loop of Multimodal Image Fusion: Trends on Deep-Learning Based Approaches |
Type |
Conference Article |
| Year |
2023 |
Publication |
IEEE 13th International Conference on Pattern Recognition Systems ICPRS 2023, julio 4-7 |
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ISBN |
979-835033337-4 |
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Approved |
no |
| Call Number |
cidis @ cidis @ |
Serial |
213 |
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| Author |
Rafael E. Rivadeneira, Henry O. Velesaca, Angel D. Sappa |
| Title |
Object Detection in Very Low-Resolution Thermal Images through a Guided-Based Super-Resolution Approach |
Type |
Conference Article |
| Year |
2023 |
Publication |
17th International Conference On Signal Image Technology & Internet Based System |
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Approved |
no |
| Call Number |
cidis @ cidis @ |
Serial |
224 |
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| Author |
Armin Mehri, Parichehr Behjati, Dario Carpio, and Angel D. Sappa |
| Title |
SRFormer: Efficient Yet Powerful Transformer Network For Single Image Super Resolution |
Type |
Journal Article |
| Year |
2023 |
Publication |
IEEE access |
Abbreviated Journal |
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| Volume |
Vol. 11 |
Issue |
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Pages |
121457 - 121469 |
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| ISSN |
21693536 |
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Approved |
no |
| Call Number |
cidis @ cidis @ |
Serial |
227 |
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| Author |
Xavier Soria, Yachuan Li, Mohammad Rouhani & Angel D. Sappa |
| Title |
Tiny and Efficient Model for the Edge Detection Generalization |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings – 2023 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2023 |
Abbreviated Journal |
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Issue |
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Pages |
1356 - 1365 |
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Approved |
no |
| Call Number |
cidis @ cidis @ |
Serial |
229 |
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| Author |
Miguel Oliveira; Vítor Santos; Angel D. Sappa; Paulo Dias; A. Paulo Moreira |
| Title |
Incremental Texture Mapping for Autonomous Driving |
Type |
Journal Article |
| Year |
2016 |
Publication |
Robotics and Autonomous Systems Journal |
Abbreviated Journal |
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| Volume |
Vol. 84 |
Issue |
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Pages |
pp. 113-128 |
| Keywords |
Scene reconstruction, Autonomous driving, Texture mapping |
| Abstract |
Autonomous vehicles have a large number of on-board sensors, not only for providing coverage all around the vehicle, but also to ensure multi-modality in the observation of the scene. Because of this, it is not trivial to come up with a single, unique representation that feeds from the data given by all these sensors. We propose an algorithm which is capable of mapping texture collected from vision based sensors onto a geometric description of the scenario constructed from data provided by 3D sensors. The algorithm uses a constrained Delaunay triangulation to produce a mesh which is updated using a specially devised sequence of operations. These enforce a partial configuration of the mesh that avoids bad quality textures and ensures that there are no gaps in the texture. Results show that this algorithm is capable of producing fine quality textures. |
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| Language |
English |
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English |
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Approved |
no |
| Call Number |
cidis @ cidis @ |
Serial |
50 |
| Permanent link to this record |
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| Author |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla |
| Title |
Image patch similarity through a meta-learning metric based approach |
Type |
Conference Article |
| Year |
2019 |
Publication |
15th International Conference on Signal Image Technology & Internet based Systems (SITIS 2019); Sorrento, Italia |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
511-517 |
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| Abstract |
Comparing images regions are one of the core methods used on computer vision for tasks like image classification, scene understanding, object detection and recognition. Hence, this paper proposes a novel approach to determine similarity of image regions (patches), in order to obtain the best representation of image patches. This problem has been studied by many researchers presenting different approaches, however, the ability to find the better criteria to measure the similarity on image regions are still a challenge. The present work tackles this problem using a few-shot metric based meta-learning framework able to compare image regions and determining a similarity measure to decide if there is similarity between the compared patches. Our model is training end-to-end from scratch. Experimental results
have shown that the proposed approach effectively estimates the similarity of the patches and, comparing it with the state of the art approaches, shows better results. |
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no |
| Call Number |
gtsi @ user @ |
Serial |
115 |
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