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Author Armin Mehri; Angel D. Sappa
Title (up) Colorizing Near Infrared Images through a Cyclic Adversarial Approach of Unpaired Samples Type Conference Article
Year 2019 Publication Conference on Computer Vision and Pattern Recognition Workshops (CVPR 2019); Long Beach, California, United States Abbreviated Journal
Volume Issue Pages 971-979
Keywords
Abstract This paper presents a novel approach for colorizing

near infrared (NIR) images. The approach is based on

image-to-image translation using a Cycle-Consistent adversarial network for learning the color channels on unpaired dataset. This architecture is able to handle unpaired datasets. The approach uses as generators tailored

networks that require less computation times, converge

faster and generate high quality samples. The obtained results have been quantitatively—using standard evaluation

metrics—and qualitatively evaluated showing considerable

improvements with respect to the state of the art
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Call Number gtsi @ user @ Serial 105
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Author Roberto Jacome Galarza; Miguel-Andrés Realpe-Robalino; Chamba-Eras LuisAntonio; Viñán-Ludeña MarlonSantiago and Sinche-Freire Javier-Francisco
Title (up) Computer vision for image understanding. A comprehensive review Type Conference Article
Year 2019 Publication International Conference on Advances in Emerging Trends and Technologies (ICAETT 2019); Quito, Ecuador Abbreviated Journal
Volume Issue Pages 248-259
Keywords
Abstract Computer Vision has its own Turing test: Can a machine describe the contents of an image or a video in the way a human being would do? In this paper, the progress of Deep Learning for image recognition is analyzed in order to know the answer to this question. In recent years, Deep Learning has increased considerably the precision rate of many tasks related to computer vision. Many datasets of labeled images are now available online, which leads to pre-trained models for many computer vision applications. In this work, we gather information of the latest techniques to perform image understanding and description. As a conclusion we obtained that the combination of Natural Language Processing (using Recurrent Neural Networks and Long Short-Term Memory) plus Image Understanding (using Convolutional Neural Networks) could bring new types of powerful and useful applications in which the computer will be able to answer questions about the content of images and videos. In order to build datasets of labeled images, we need a lot of work and most of the datasets are built using crowd work. These new applications have the potential to increase the human machine interaction to new levels of usability and user’s satisfaction.
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Call Number gtsi @ user @ Serial 97
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Author Sebastián Fuenzalida; Keyla Toapanta; Jonathan S. Paillacho Corredores; Dennys Paillacho
Title (up) Forward and Inverse Kinematics of a Humanoid Robot Head for Social Human Robot-Interaction Type Conference Article
Year 2019 Publication IEEE ETCM 2019 Fourth Ecuador Technical Chapters Meeting; Guayaquil, Ecuador Abbreviated Journal
Volume Issue Pages
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Abstract This paper presents an analysis of forward and inverse kinematics for a humanoid robotic head. The robotic head is used for the study of social human-robot interaction, such as a support tool to maintain the attention of patients with Autism Spectrum Disorder. The design of a parallel robot that emulates human head movements through a closed structure is presented. The position and orientation in this space is controlled by three servomotors. For this, the solutions made for the kinematic problem are encompassed by a geometric analysis of a mobile base. This article describes a non-systematic method,

called the geometric method, and compares some of the most popular existing methods considering reliability and computational cost. The geometric method avoids the use of changing reference systems, and instead uses geometric

relationships to directly obtain the position based on joint variables; and the other way around. Therefore, it converges in a few iterations and has a low computational cost.
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Call Number gtsi @ user @ Serial 113
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Author Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla
Title (up) 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
Volume Issue Pages 511-517
Keywords
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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Call Number gtsi @ user @ Serial 115
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Author Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla; Riad I. Hammoud
Title (up) Image Vegetation Index through a Cycle Generative Adversarial Network Type Conference Article
Year 2019 Publication Conference on Computer Vision and Pattern Recognition Workshops (CVPR 2019); Long Beach, California, United States Abbreviated Journal
Volume Issue Pages 1014-1021
Keywords
Abstract This paper proposes a novel approach to estimate the

Normalized Difference Vegetation Index (NDVI) just from

an RGB image. The NDVI values are obtained by using

images from the visible spectral band together with a synthetic near infrared image obtained by a cycled GAN. The

cycled GAN network is able to obtain a NIR image from

a given gray scale image. It is trained by using unpaired

set of gray scale and NIR images by using a U-net architecture and a multiple loss function (gray scale images are

obtained from the provided RGB images). Then, the NIR

image estimated with the proposed cycle generative adversarial network is used to compute the NDVI index. Experimental results are provided showing the validity of the proposed approach. Additionally, comparisons with previous

approaches are also provided.
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Call Number gtsi @ user @ Serial 106
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Author Jorge Alvarez Tello; Mireya Zapata; Dennys Paillacho
Title (up) Kinematic optimization of a robot head movements for the evaluation of human-robot interaction in social robotics. Type Conference Article
Year 2019 Publication 10th International Conference on Applied Human Factors and Ergonomics and the Affiliated Conferences (AHFE 2019), Washington D.C.; United States. Advances in Intelligent Systems and Computing Abbreviated Journal
Volume 975 Issue Pages 108-118
Keywords
Abstract This paper presents the simplification of the head movements from

the analysis of the biomechanical parameters of the head and neck at the

mechanical and structural level through CAD modeling and construction with

additive printing in ABS/PLA to implement non-verbal communication strategies and establish behavior patterns in the social interaction. This is using in the

denominated MASHI (Multipurpose Assistant robot for Social Human-robot

Interaction) experimental robotic telepresence platform, implemented by a

display with a fish-eye camera along with the mechanical mechanism, which

permits 4 degrees of freedom (DoF). In the development of mathematicalmechanical modeling for the kinematics codification that governs the robot and

the autonomy of movement, we have the Pitch, Roll, and Yaw movements, and

the combination of all of them to establish an active communication through

telepresence. For the computational implementation, it will be show the rotational matrix to describe the movement.
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Call Number gtsi @ user @ Serial 108
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Author Jorge Alvarez; Mireya Zapata; Dennys Paillacho
Title (up) Mechanical Design of a spatial mechanism for the robot head movements in social robotics for the evaluation of Human-Robot Interaction. Type Conference Article
Year 2019 Publication 2nd International Conference on Human Systems Engineering and Design: Future Trends and Applications (IHSED 2019); Munich, Alemania Abbreviated Journal
Volume 1026 Issue Pages 160-165
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Call Number gtsi @ user @ Serial 104
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Author W. Agila; Gomer Rubio; L. Miranda; D. Sanaguano
Title (up) Open Control Architecture for the Characterization and Control of the PEM Fuel Cell Type Conference Article
Year 2019 Publication IEEE ETCM 2019 Fourth Ecuador Technical Chapters Meeting; Guayaquil, Ecuador Abbreviated Journal
Volume Issue Pages 1-5
Keywords PEM fuel cell, Experimental System, Control Engineering.
Abstract Proton exchange membrane (PEM) fuel cells, are an efficient and clean source of electrical energy. The analysis of its operation requires experimental work, which allows measuring, modeling and optimizing PEM fuel cells electrical behavior under different operating conditions. Therefore, having an experimentation platform that allows to easily carry out its study and control is essential. This research presents the design and development of an open instrumental system that allows measuring, controlling and determining the operating parameters of a PEM fuel cell. As results, the polarization curves, voltage-current, obtained by the system itself in different experimental conditions are shown. These curves are a very useful tool to evaluate the electrical behavior of the PEM battery.
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Call Number gtsi @ user @ Serial 118
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Author Miguel Realpe; Jonathan S. Paillacho Corredores; Joe Saverio & Allan Alarcon
Title (up) Open Source system for identification of corn leaf chlorophyll contents based on multispectral images Type Conference Article
Year 2019 Publication International Conference on Applied Technologies (ICAT 2019); Quito, Ecuador Abbreviated Journal
Volume Issue Pages 572-581
Keywords
Abstract It is important for farmers to know the level of chlorophyll in plants since this depends on the treatment they should give to their crops. There are two common classic methods to get chlorophyll values: from laboratory analysis and electronic devices. Both methods obtain the chlorophyll level of one sample at a time, although they can be destructive. The objective of this research is to develop a system that allows obtaining the chlorophyll level of plants using images.

Python programming language and different libraries of that language were used to develop the solution. It was decided to implement an image labeling module, a simple linear regression and a prediction module. The first module was used to create a database that links the values of the images with those of chlorophyll, which was then used to obtain linear regression in order to determine the relationship between these variables. Finally, the linear

regression was used in the prediction system to obtain chlorophyll values from the images. The linear regression was trained with 92 images, obtaining a root-mean-square error of 7.27 SPAD units. While the testing was perform using 10 values getting a maximum error of 15.5%.

It is concluded that the system is appropriate for chlorophyll contents identification of corn leaves in field tests.

However, it can also be adapted for other measurement and crops. The system can be downloaded at github.com/JoeSvr95/NDVI-Checking [1].
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Call Number gtsi @ user @ Serial 116
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Author Raul A. Mira; Patricia L. Suarez; Rafael E. Rivadeneira; Angel D. Sappa
Title (up) PETRA: A Crowdsourcing-Based Platform for Rocks Data Collection and Characterization Type Conference Article
Year 2019 Publication IEEE ETCM 2019 Fourth Ecuador Technical Chapters Meeting; Guayaquil, Ecuador Abbreviated Journal
Volume Issue Pages 1-6
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Abstract This paper presents details of a distributed platform intended for data acquisition, evaluation, storage and visualization, which is fully implemented under the crowdsourcing paradigm. The proposed platform is the result from collaboration between computer science and petrology researchers and it is intended for academic purposes. The platform is designed within a MTV (Model, Template and View) architecture and also designed for a collaborative data store and managing of rocks from multiple readers and writers, taking advantage of ubiquity of web applications, and neutrality of researchers from different

communities to validate the data. The platform is being used and validated by students and academics from our university; in the near future it will be open to other users interested on this topic.
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Notes Approved no
Call Number gtsi @ user @ Serial 112
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