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Author Patricia L. Suárez, Angel D. Sappa and Boris X. Vintimilla
Title Deep learning-based vegetation index estimation Type (up) Book Chapter
Year 2021 Publication Generative Adversarial Networks for Image-to-Image Translation Book. Abbreviated Journal
Volume Chapter 9 Issue Issue 2 Pages 205-232
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Call Number cidis @ cidis @ Serial 137
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Author Henry O. Velesaca, Patricia L. Suárez, Dario Carpio, Rafael E. Rivadeneira, Ángel Sánchez, Angel D. Sappa.
Title Video Analytics in Urban Environments: Challenges and Approaches. Type (up) Book Chapter
Year 2022 Publication ICT Applications for Smart Cities Part of the Intelligent Systems Reference Library book series Abbreviated Journal BOOK
Volume 224 Issue Pages 101-122
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Call Number cidis @ cidis @ Serial 196
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Author Jorge L. Charco, Angel D. Sappa, Boris X. Vintimilla, Henry O. Velesaca.
Title Human Body Pose Estimation in Multi-view Environments. Type (up) Book Chapter
Year 2022 Publication ICT Applications for Smart Cities Part of the Intelligent Systems Reference Library book series Abbreviated Journal BOOK
Volume 224 Issue Pages 79-99
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Call Number cidis @ cidis @ Serial 197
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Author Angel D. Sappa.
Title ICT Applications for Smart Cities Type (up) Book Chapter
Year 2022 Publication Intelligent Systems Reference Library Abbreviated Journal BOOK
Volume 224 Issue Pages
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Call Number cidis @ cidis @ Serial 198
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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 (up) Conference Article
Year 2017 Publication 19th International Conference on Image Analysis and Processing. Abbreviated Journal
Volume Issue Pages 287-297
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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 (up) Conference Article
Year 2017 Publication 2nd IEEE Ecuador Tehcnnical Chapters Meeting (ETCM) Abbreviated Journal
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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 (up) 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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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 (up) 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
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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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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 (up) 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
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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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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 (up) Conference Article
Year 2018 Publication 25 th IEEE International Conference on Image Processing, ICIP 2018 Abbreviated Journal
Volume Issue Pages 2237-2241
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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.
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Call Number gtsi @ user @ Serial 81
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