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Author Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla pdf  openurl
  Title 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 (down) Issue Pages 511-517  
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  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 Miguel Realpe; Jonathan S. Paillacho Corredores; Joe Saverio & Allan Alarcon pdf  openurl
  Title 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 (down) Issue Pages 572-581  
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  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 W. Agila; Gomer Rubio; L. Miranda; D. Sanaguano pdf  openurl
  Title 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 (down) 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 Xavier Soria; Edgar Riba; Angel D. Sappa pdf  isbn
openurl 
  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 (down) Issue 9093290 Pages 1912-1921  
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  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.  
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  ISSN ISBN 978-172816553-0 Medium  
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  Call Number cidis @ cidis @ Serial 126  
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Author Patricia L. Suarez pdf  openurl
  Title Procesamiento y representación de imágenes multiespectrales usando técnicas de aprendizaje profundo (Ph.D. Angel Sappa, Director & Ph.D. Boris Vintimilla, Codirector.). Ph.D. thesis. Type Book Chapter
  Year 2020 Publication Ediciones FIEC-ESPOL. Abbreviated Journal  
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  Corporate Author Ph.D. Angel Sappa, Director & Ph.D. Boris Vintimilla, Codirector. Thesis  
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  Language Español Summary Language Original Title  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 144  
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Author Rosero Vasquez Shendry url  openurl
  Title Facial recognition: traditional methods vs. methods based on deep learning. Advances in Intelligent Systems and Computing – Information Technology and Systems Proceedings of ICITS 2020. Type Journal Article
  Year 2020 Publication Abbreviated Journal  
  Volume (down) Issue Pages 615-625  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 145  
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Author Mehri, A, Ardakani, P.B., Sappa, A.D. pdf  openurl
  Title MPRNet: Multi-Path Residual Network for Lightweight Image Super Resolution. Type Conference Article
  Year 2021 Publication In IEEE Winter Conference on Applications of Computer Vision WACV 2021, enero 5-9, 2021 Abbreviated Journal  
  Volume (down) Issue Pages 2703-2712  
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  Call Number cidis @ cidis @ Serial 148  
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Author Mehri, A, Ardakani, P.B., Sappa, A.D. pdf  openurl
  Title LiNet: A Lightweight Network for Image Super Resolution Type Conference Article
  Year 2021 Publication 25th International Conference on Pattern Recognition (ICPR), enero 10-15, 2021 Abbreviated Journal  
  Volume (down) Issue Pages 7196-7202  
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  Call Number cidis @ cidis @ Serial 149  
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Author Jacome-Galarza L.-R pdf  openurl
  Title Crop yield prediction utilizing multimodal deep learning Type Conference Article
  Year 2021 Publication 16th Iberian Conference on Information Systems and Technologies, CISTI 2021, junio 23 – 26, 2021 Abbreviated Journal  
  Volume (down) Issue Pages  
  Keywords Agricultura de precisión; sensores remotos; aprendizaje profundo multimodal; IoT; agentes inteligentes; computación aplicada.  
  Abstract La agricultura de precisión es una práctica vital para

mejorar la producción de cosechas. El presente trabajo tiene

como objetivo desarrollar un modelo multimodal de aprendizaje

profundo que es capaz de producir un mapa de salud de

cosechas. El modelo recibe como entradas imágenes multiespectrales

y datos de sensores de campo (humedad,

temperatura, estado del suelo, etc.) y crea un mapa de

rendimiento de la cosecha. La utilización de datos multimodales

tiene como finalidad extraer patrones ocultos del estado de salud

de las cosechas y de esta manera obtener mejores resultados que

los obtenidos mediante los índices de vegetación.
 
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 150  
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Author Rivadeneira R.E., Sappa A.D., Vintimilla B.X., Nathan S., Kansal P., Mehri A et al. pdf  openurl
  Title Thermal Image Super-Resolution Challenge – PBVS 2021. Type Conference Article
  Year 2021 Publication In IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021., junio 19 – 25, 2021 Abbreviated Journal  
  Volume (down) Issue Pages 4354-4362  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 151  
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