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Author Santos V.; Angel D. Sappa.; Oliveira M. & de la Escalera A.
Title Special Issue on Autonomous Driving and Driver Assistance Systems Type (down) 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 Cristhian A. Aguilera, Cristhian Aguilera, Cristóbal A. Navarro, & Angel D. Sappa
Title Fast CNN Stereo Depth Estimation through Embedded GPU Devices Type (down) 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
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 14248220 ISBN Medium
Area Expedition Conference
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 (down) 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
Series Volume Series Issue Edition
ISSN ISBN 14248220 Medium
Area Expedition Conference
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 SRFormer: Efficient Yet Powerful Transformer Network For Single Image Super Resolution Type (down) Journal Article
Year 2023 Publication IEEE access Abbreviated Journal
Volume Vol. 11 Issue Pages 121457 - 121469
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 21693536 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number cidis @ cidis @ Serial 227
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Author Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla
Title Colorizing Infrared Images through a Triplet Condictional DCGAN Architecture Type (down) Conference Article
Year 2017 Publication 19th International Conference on Image Analysis and Processing. Abbreviated Journal
Volume Issue Pages 287-297
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Series Editor Series Title Abbreviated Series Title
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Notes Approved no
Call Number gtsi @ user @ Serial 66
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Author Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla
Title Learning Image Vegetation Index through a Conditional Generative Adversarial Network Type (down) Conference Article
Year 2017 Publication 2nd IEEE Ecuador Tehcnnical Chapters Meeting (ETCM) Abbreviated Journal
Volume Issue Pages
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Abstract
Address
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference
Notes Approved no
Call Number gtsi @ user @ Serial 70
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Author Xavier Soria; Angel D. Sappa; Arash Akbarinia
Title Multispectral Single-Sensor RGB-NIR Imaging: New Challenges an Oppotunities Type (down) Conference Article
Year 2017 Publication The 7th International Conference on Image Processing Theory, Tools and Application Abbreviated Journal
Volume Issue Pages 1-6
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Abstract
Address
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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Notes Approved no
Call Number gtsi @ user @ Serial 72
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Author Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla; Riad I. Hammoud
Title Deep Learning based Single Image Dehazing Type (down) Conference Article
Year 2018 Publication 14th IEEE Workshop on Perception Beyond the Visible Spectrum – In conjunction with CVPR 2018. Salt Lake City, Utah. USA Abbreviated Journal
Volume Issue Pages
Keywords
Abstract This paper proposes a novel approach to remove haze

degradations in RGB images using a stacked conditional

Generative Adversarial Network (GAN). It employs a triplet

of GAN to remove the haze on each color channel independently.

A multiple loss functions scheme, applied over a

conditional probabilistic model, is proposed. The proposed

GAN architecture learns to remove the haze, using as conditioned

entrance, the images with haze from which the clear

images will be obtained. Such formulation ensures a fast

model training convergence and a homogeneous model generalization.

Experiments showed that the proposed method

generates high-quality clear images.
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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 gtsi @ user @ Serial 83
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Author Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla
Title Vegetation Index Estimation from Monospectral Images Type (down) Conference Article
Year 2018 Publication 15th International Conference, Image Analysis and Recognition (ICIAR 2018), Póvoa de Varzim, Portugal. Lecture Notes in Computer Science Abbreviated Journal
Volume 10882 Issue Pages 353-362
Keywords
Abstract This paper proposes a novel approach to estimate Normalized

Difference Vegetation Index (NDVI) from just the red channel of

a RGB image. The NDVI index is defined as the ratio of the difference

of the red and infrared radiances over their sum. In other words, information

from the red channel of a RGB image and the corresponding

infrared spectral band are required for its computation. In the current

work the NDVI index is estimated just from the red channel by training a

Conditional Generative Adversarial Network (CGAN). The architecture

proposed for the generative network consists of a single level structure,

which combines at the final layer results from convolutional operations

together with the given red channel with Gaussian noise to enhance

details, resulting in a sharp NDVI image. Then, the discriminative model

estimates the probability that the NDVI generated index came from the

training dataset, rather than the index automatically generated. Experimental

results with a large set of real images are provided showing that

a Conditional GAN single level model represents an acceptable approach

to estimate NDVI index.
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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 gtsi @ user @ Serial 82
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Author Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla; Riad I. Hammoud
Title Near InfraRed Imagery Colorization Type (down) Conference Article
Year 2018 Publication 25 th IEEE International Conference on Image Processing, ICIP 2018 Abbreviated Journal
Volume Issue Pages 2237-2241
Keywords
Abstract This paper proposes a stacked conditional Generative

Adversarial Network-based method for Near InfraRed

(NIR) imagery colorization. We propose a variant architecture

of Generative Adversarial Network (GAN) that uses multiple

loss functions over a conditional probabilistic generative model.

We show that this new architecture/loss-function yields better

generalization and representation of the generated colored IR

images. The proposed approach is evaluated on a large test

dataset and compared to recent state of the art methods using

standard metrics.1

Index Terms—Convolutional Neural Networks (CNN), Generative

Adversarial Network (GAN), Infrared Imagery colorization.
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 gtsi @ user @ Serial 81
Permanent link to this record