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Patricia L. Suarez, Angel D. Sappa, Boris X. Vintimilla, & Riad I. Hammoud. (2019). Image Vegetation Index through a Cycle Generative Adversarial Network. In Conference on Computer Vision and Pattern Recognition Workshops (CVPR 2019); Long Beach, California, United States (pp. 1014–1021).
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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Armin Mehri, & Angel D. Sappa. (2019). Colorizing Near Infrared Images through a Cyclic Adversarial Approach of Unpaired Samples. In Conference on Computer Vision and Pattern Recognition Workshops (CVPR 2019); Long Beach, California, United States (pp. 971–979).
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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Miguel Realpe, Jonathan S. Paillacho Corredores, & Joe Saverio & Allan Alarcon. (2019). Open Source system for identification of corn leaf chlorophyll contents based on multispectral images. In International Conference on Applied Technologies (ICAT 2019); Quito, Ecuador (pp. 572–581).
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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Patricia L. Suarez, Angel D. Sappa, & Boris X. Vintimilla. (2019). Image patch similarity through a meta-learning metric based approach. In 15th International Conference on Signal Image Technology & Internet based Systems (SITIS 2019); Sorrento, Italia (pp. 511–517).
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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Wilton Agila, Gomer Rubio, Francisco Vidal, & B. Lima. (2019). Real time Qualitative Model for estimate Water content in PEM Fuel Cell. In 8th International Conference on Renewable Energy Research and Applications (ICRERA 2019); Brasov, Rumania (pp. 455–459).
Abstract: To maintain optimum performance of the electrical
response of a fuel cell, a real time identification of the
malfunction situations is required. Critical fuel cell states depend,
among others, on the variable demand of electric load and are
directly related to the membrane hydration level. The real time
perception of relevant states in the PEM fuel cell states space, is
still a challenge for the PEM fuel cell control systems. Current
work presents the design and implementation of a methodology
based upon fuzzy decision techniques that allows real time
characterization of the dehydration and flooding states of a PEM
fuel cell. Real time state estimation is accomplished through a
perturbation-perception process on the PEM fuel cell and further
on voltage oscillation analysis. The real time implementation of
the perturbation-perception algorithm to detect PEM fuel cell
critical states is a novelty and a step forwards the control of the
PEM fuel cell to reach and maintain optimal performance.
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Rafael E. Rivadeneira, Patricia L. Suarez, Angel D. Sappa, & Boris X. Vintimilla. (2019). Thermal Image SuperResolution through Deep Convolutional Neural Network. In 16th International Conference on Image Analysis and Recognition (ICIAR 2019); Waterloo, Canadá (pp. 417–426).
Abstract: Due to the lack of thermal image datasets, a new dataset has been acquired for proposed a superesolution approach using a Deep Convolution Neural Network schema. In order to achieve this image enhancement process a new thermal images dataset is used. Di?erent experiments have been carried out, ?rstly, the proposed architecture has been trained using only images of the visible spectrum, and later it has been trained with images of the thermal spectrum, the results showed that with the network trained with thermal images, better results are obtained in the process of enhancing the images, maintaining the image details and perspective. The thermal dataset is available at http://www.cidis.espol.edu.ec/es/dataset
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G.A. Rubio, & Wilton Agila. (2019). Transients analysis in Proton Exchange Membrane Fuel Cells: A critical review. In 8th International Conference on Renewable Energy Research and Applications (ICRERA 2019); Brasov, Rumania (pp. 249–252).
Abstract: When a proton exchange fuel cell operates it produces in addition to electrical
energy, heat and water as sub products, which impact on the performance of the cell. This
paper analyzes the issue of transients and proposes a model that describes the dynamic
operation of the fuel cell. The model considers the transients produced by electrochemical
reactions, by flow water and by heat transfer. Two-phase flow transients result in
increased the parasitic power losses and thermal transients may result in flooding or dryout of the GDL and membrane, understanding transient behavior is critical for reliable
and predictable performance from the cell.
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Roberto Jacome Galarza, Miguel-Andrés Realpe-Robalino, Chamba-Eras LuisAntonio, & Viñán-Ludeña MarlonSantiago and Sinche-Freire Javier-Francisco. (2019). Computer vision for image understanding. A comprehensive review. In International Conference on Advances in Emerging Trends and Technologies (ICAETT 2019); Quito, Ecuador (pp. 248–259).
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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Angel Morera, Angel Sánchez, Angel D. Sappa, & José F. Vélez. (2019). Robust Detection of Outdoor Urban Advertising Panels in Static Images. In 17th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS 2019); Ávila, España. Communications in Computer and Information Science (Vol. 1047, pp. 246–256).
Abstract: One interesting publicity application for Smart City environments is recognizing brand information contained in urban advertising
panels. For such a purpose, a previous stage is to accurately detect and
locate the position of these panels in images. This work presents an effective solution to this problem using a Single Shot Detector (SSD) based
on a deep neural network architecture that minimizes the number of
false detections under multiple variable conditions regarding the panels and the scene. Achieved experimental results using the Intersection
over Union (IoU) accuracy metric make this proposal applicable in real
complex urban images.
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G.A. Rubio, & Wilton Agila. (2019). Sustainable Energy: A Strategic View of Fuel Cells. In 8th International Conference on Renewable Energy Research and Applications (ICRERA 2019); Brasov, Rumania (pp. 239–243).
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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