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Author Suárez P.
Title Processing and Representation of Multispectral Images Using Deep Learning Techniques Type Magazine Article
Year 2021 Publication In Electronic Letters on Computer Vision and Image Analysis Abbreviated Journal
Volume Vol. 19 Issue Issue 2 Pages pp. 5-8
Keywords
Abstract
Address
Corporate Author Ph.D. Angel Sappa, Director & Ph.D. Boris Vintimilla, Codirector Thesis Master's thesis
Publisher Place of Publication Editor
Language Español Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes Approved yes
Call Number (up) cidis @ cidis @ Serial 122
Permanent link to this record
 

 
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 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
Keywords
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.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 21607508 ISBN 978-172819360-1 Medium
Area Expedition Conference
Notes Approved no
Call Number (up) cidis @ cidis @ Serial 123
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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
Volume Issue 9093290 Pages 1912-1921
Keywords
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.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-172816553-0 Medium
Area Expedition Conference
Notes Approved no
Call Number (up) cidis @ cidis @ Serial 126
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Author Henry O. Velesaca; Raul A. Mira; Patricia L. Suarez; Christian X. Larrea; Angel D. Sappa.
Title Deep Learning based Corn Kernel Classification. Type Conference Article
Year 2020 Publication The 1st International Workshop and Prize Challenge on Agriculture-Vision: Challenges & Opportunities for Computer Vision in Agriculture on the Conference Computer on Vision and Pattern Recongnition (CVPR 2020) Abbreviated Journal
Volume 2020-June Issue 9150684 Pages 294-302
Keywords
Abstract This paper presents a full pipeline to classify sample sets of corn kernels. The proposed approach follows a segmentation-classification scheme. The image segmentation is performed through a well known deep learning based

approach, the Mask R-CNN architecture, while the classification is performed by means of a novel-lightweight network specially designed for this task—good corn kernel, defective corn kernel and impurity categories are considered.

As a second contribution, a carefully annotated multitouching corn kernel dataset has been generated. This dataset has been used for training the segmentation and

the classification modules. Quantitative evaluations have been performed and comparisons with other approaches provided showing improvements with the proposed pipeline.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 21607508 ISBN 978-172819360-1 Medium
Area Expedition Conference
Notes Approved no
Call Number (up) cidis @ cidis @ Serial 124
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Author Henry O. Velesaca, Steven Araujo, Patricia L. Suarez, Ángel Sanchez & Angel D. Sappa
Title Off-the-Shelf Based System for Urban Environment Video Analytics. Type Conference Article
Year 2020 Publication The 27th International Conference on Systems, Signals and Image Processing (IWSSIP 2020) Abbreviated Journal
Volume 2020-July Issue 9145121 Pages 459-464
Keywords Greenhouse gases, carbon footprint, object detection, object tracking, website framework, off-the-shelf video analytics.
Abstract This paper presents the design and implementation details of a system build-up by using off-the-shelf algorithms for urban video analytics. The system allows the connection to public video surveillance camera networks to obtain the necessary

information to generate statistics from urban scenarios (e.g., amount of vehicles, type of cars, direction, numbers of persons, etc.). The obtained information could be used not only for traffic management but also to estimate the carbon footprint of urban scenarios. As a case study, a university campus is selected to

evaluate the performance of the proposed system. The system is implemented in a modular way so that it is being used as a testbed to evaluate different algorithms. Implementation results are provided showing the validity and utility of the proposed approach.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 21578672 ISBN 978-172817539-3 Medium
Area Expedition Conference
Notes Approved no
Call Number (up) cidis @ cidis @ Serial 125
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Author Nayeth I. Solorzano Alcivar, Robert Loor, Stalyn Gonzabay Yagual, & Boris X. Vintimilla
Title 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
Keywords
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.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number (up) cidis @ cidis @ Serial 131
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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.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 14248220 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number (up) 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.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 14248220 Medium
Area Expedition Conference
Notes Approved no
Call Number (up) cidis @ cidis @ Serial 133
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Author Dennys Paillacho; Nayeth I. Solorzano Alcivar; Jonathan S. Paillacho Corredores
Title LOLY 1.0: A Proposed Human-Robot-Game Platform Architecture for the Engagement of Children with Autism in the Learning Process Type Book Chapter
Year 2021 Publication The international Conference on Systems and Information Sciences (ICCIS 2020), julio 27-29. Advances in Intelligent Systems and Computing. Abbreviated Journal
Volume 1273 Issue Pages 225-238
Keywords
Abstract
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 21945357 ISBN 978-303059193-9 Medium
Area Expedition Conference
Notes Approved yes
Call Number (up) cidis @ cidis @ Serial 185
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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
Keywords
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.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number (up) cidis @ cidis @ Serial 139
Permanent link to this record