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Author Rafael E. Rivadeneira; Angel D. Sappa; Boris X. Vintimilla; Lin Guo; Jiankun Hou; Armin Mehri; Parichehr Behjati; Ardakani Heena Patel; Vishal Chudasama; Kalpesh Prajapati; Kishor P. Upla; Raghavendra Ramachandra; Kiran Raja; Christoph Busch; Feras Almasri; Olivier Debeir; Sabari Nathan; Priya Kansal; Nolan Gutierrez; Bardia Mojra; William J. Beksi
Title (down) Thermal Image Super-Resolution Challenge – PBVS 2020 Type Conference Article
Year 2020 Publication The 16th IEEE Workshop on Perception Beyond the Visible Spectrum on the Conference on Computer Vision and Pattern Recongnition (CVPR 2020) Abbreviated Journal
Volume 2020-June Issue 9151059 Pages 432-439
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Abstract This paper summarizes the top contributions to the first challenge on thermal image super-resolution (TISR) which was organized as part of the Perception Beyond the Visible Spectrum (PBVS) 2020 workshop. In this challenge, a novel thermal image dataset is considered together with stateof-the-art approaches evaluated under a common framework.

The dataset used in the challenge consists of 1021 thermal images, obtained from three distinct thermal cameras at different resolutions (low-resolution, mid-resolution, and high-resolution), resulting in a total of 3063 thermal images. From each resolution, 951 images are used for training and 50 for testing while the 20 remaining images are used for two proposed evaluations. The first evaluation consists of downsampling the low-resolution, midresolution, and high-resolution thermal images by x2, x3 and x4 respectively, and comparing their super-resolution

results with the corresponding ground truth images. The second evaluation is comprised of obtaining the x2 superresolution from a given mid-resolution thermal image and comparing it with the corresponding semi-registered highresolution thermal image. Out of 51 registered participants, 6 teams reached the final validation phase.
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Language English Summary Language Original Title
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ISSN 21607508 ISBN 978-172819360-1 Medium
Area Expedition Conference
Notes Approved no
Call Number cidis @ cidis @ Serial 123
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Author Rafael E. Rivadeneira, Angel D. Sappa, Chenyang Wang, Junjun Jiang, Zhiwei Zhong, Peilin Chen & Shiqi Wang
Title (down) Thermal Image Super Resolution Challenge Results – PBVS 2024 Type Conference Article
Year 2024 Publication IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops CVPRW 2024 Seattle 16 – 22 June 2024 Abbreviated Journal
Volume Issue Pages 3113 – 3122
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Series Volume Series Issue Edition
ISSN ISBN 979-835036547-4 Medium
Area Expedition Conference
Notes Approved no
Call Number cidis @ cidis @ Serial 233
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Author Patricia Suarez & Angel D. Sappa
Title (down) Synthetic Thermal Image Generation from Multi-Cue Input Data 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 265
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Author Henry O. Velesaca, Patricia L. Suarez, Dario Carpio, and Angel D. Sappa
Title (down) Synthesized Image Datasets: Towards an Annotation-Free Instance Segmentation Strategy Type Conference Article
Year 2021 Publication 16 International Symposium on Visual Computing. Octubre 4-6, 2021. Lecture Notes in Computer Science Abbreviated Journal
Volume 13017 Issue Pages 131-143
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Notes Approved no
Call Number cidis @ cidis @ Serial 163
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Author G.A. Rubio; Wilton Agila
Title (down) Sustainable Energy: A Strategic View of Fuel Cells Type Conference Article
Year 2019 Publication 8th International Conference on Renewable Energy Research and Applications (ICRERA 2019); Brasov, Rumania Abbreviated Journal
Volume Issue Pages 239-243
Keywords
Abstract Based on the model of the proton exchange fuel cell in a strategic context,

this document develops the issue of energy as one of the pillars to achieve the

sustainability of our planet, considering the future scenarios up to the year 2060 of the

situation energy, hydrogen as a strategic vector and the contribution of the fuel cell in

solving the serious problems of environmental pollution and economic inequity that

humanity faces; for its application in the energy generation, telecommunications and

vehicle manufacturing industries.
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Call Number gtsi @ user @ Serial 110
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Author Nayeth I. Solorzano Alcivar, Robert Loor, Stalyn Gonzabay Yagual, & Boris X. Vintimilla
Title (down) Statistical Representations of a Dashboard to Monitor Educational Videogames in Natural Language Type Conference Article
Year 2020 Publication ETLTC – ACM Chapter: International Conference on Educational Technology, Language and Technical Communication; Fukushima, Japan, 27-31 Enero 2020 Abbreviated Journal
Volume 77 Issue Pages
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Abstract This paper explains how Natural Language (NL) processing by computers, through smart

programs as a way of Machine Learning (ML), can represent large sets of quantitative data as written

statements. The study recognized the need to improve the implemented web platform using a

dashboard in which we collected a set of extensive data to measure assessment factors of using

children´s educational games. In this case, applying NL is a strategy to give assessments, build, and

display more precise written statements to enhance the understanding of children´s gaming behavior.

We propose the development of a new tool to assess the use of written explanations rather than a

statistical representation of feedback information for the comprehension of parents and teachers with

a lack of primary level knowledge in statistics. Applying fuzzy logic theory, we present verbatim

explanations of children´s behavior playing educational videogames as NL interpretation instead of

statistical representations. An educational series of digital game applications for mobile devices,

identified as MIDI (Spanish acronym of “Interactive Didactic Multimedia for Children”) linked to a

dashboard in the cloud, is evaluated using the dashboard metrics. MIDI games tested in local primary

schools helps to evaluate the results of using the proposed tool. The guiding results allow analyzing

the degrees of playability and usability factors obtained from the data produced when children play a

MIDI game. The results obtained are presented in a comprehensive guiding evaluation report

applying NL for parents and teachers. These guiding evaluations are useful to enhance children's

learning understanding related to the school curricula applied to ludic digital games.
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Call Number cidis @ cidis @ Serial 131
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Author Ángel Morera, Ángel Sánchez, A. Belén Moreno, Angel D. Sappa, & José F. Vélez
Title (down) 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
Series Editor Series Title Abbreviated Series 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 Armin Mehri, Parichehr Behjati, Dario Carpio, and Angel D. Sappa
Title (down) SRFormer: Efficient Yet Powerful Transformer Network For Single Image Super Resolution Type Journal Article
Year 2023 Publication IEEE access Abbreviated Journal
Volume Vol. 11 Issue Pages 121457 - 121469
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Series Volume Series Issue Edition
ISSN 21693536 ISBN Medium
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Notes Approved no
Call Number cidis @ cidis @ Serial 227
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Author Santos V.; Angel D. Sappa.; Oliveira M. & de la Escalera A.
Title (down) Special Issue on Autonomous Driving and Driver Assistance Systems Type Journal Article
Year 2019 Publication In Robotics and Autonomous Systems Abbreviated Journal
Volume 121 Issue Pages
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Notes Approved no
Call Number gtsi @ user @ Serial 119
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Author Victor Santos; Angel D. Sappa; Miguel Oliveira
Title (down) Special Issue on Autonomous Driving an Driver Assistance Systems Type Journal Article
Year 2017 Publication In Robotics and Autonomous Systems Journal Abbreviated Journal
Volume Vol. 91 Issue Pages pp. 208-209
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Abstract
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Publisher Place of Publication Editor
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Notes Approved no
Call Number gtsi @ user @ Serial 65
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