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Author Abel Rubio, Wilton Agila, Leandro González & Jonathan Aviles-Cedeno pdf  openurl
  Title Distributed Intelligence in Autonomous PEM Fuel Cell Control. Type Journal Article
  Year 2023 Publication Energies 2023 Abbreviated Journal  
  Volume Vol. 16 Issue Issue 12 Pages  
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  ISSN (down) 19961073 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial 217  
Permanent link to this record
 

 
Author Dennys Paillacho, Nayeth Solórzano, Michael Arce, María Plues & Edwin Eras pdf  isbn
openurl 
  Title Advanced metrics to evaluate autistic children's attention and emotions from facial characteristics using a human robot-game interface Type Conference Article
  Year 2023 Publication Communications in Computer and Information Science. 11th Conferencia Ecuatoriana de Tecnologías de la Información y Comunicación (TICEC 2023) Cuenca 18-20 Octubre 2023 Abbreviated Journal  
  Volume 1885 CCIS Issue Pages 234 - 247  
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  ISSN (down) 18650929 ISBN 978-303145437-0 Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial 221  
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Author Cristhian A. Aguilera, Cristhian Aguilera, Cristóbal A. Navarro, & Angel D. Sappa pdf  openurl
  Title Fast CNN Stereo Depth Estimation through Embedded GPU Devices Type 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  
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  ISSN (down) 14248220 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial 132  
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Author Henry O. Velesaca, Gisel Bastidas, Mohammad Rouhani, Angel D. Sappa pdf  doi
openurl 
  Title Multimodal image registration techniques: a comprehensive survey Type Journal Article
  Year 2024 Publication Multimedia Tools and Applications Abbreviated Journal  
  Volume Vol. 83 Issue Pages 63919 - 63947  
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  ISSN (down) 13807501 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial 248  
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Author Steven Silva, Nervo Verdezoto, Dennys Paillacho, Samuel Millan-Norman & Juan David Hernandez pdf  isbn
openurl 
  Title Online Social Robot Navigation in Indoor, Large and Crowded Environments. Type Conference Article
  Year 2023 Publication IEEE International Conference on Robotics and Automation (ICRA 2023) Londres, 29 may 2023 – 2 jun 2023 Abbreviated Journal  
  Volume 2023-May Issue Pages 9749 - 9756  
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  ISSN (down) 10504729 ISBN 979-835032365-8 Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial 206  
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Author Suarez Patricia; Carpio Dario; Sappa Angel D. pdf  isbn
openurl 
  Title A Deep Learning Based Approach for Synthesizing Realistic Depth Maps Type Conference Article
  Year 2023 Publication Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics 22nd International Conference on Image Analysis and Processing, ICIAP 2023 Udine 11 – 15 September 2023 Abbreviated Journal  
  Volume 14234 LNCS Issue Pages 369 - 380  
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  ISSN (down) 03029743 ISBN 978-303143152-4 Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial 231  
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Author Emmanuel F. Morán, Boris X. Vintimilla, Miguel A. Realpe pdf  url
doi  isbn
openurl 
  Title Towards a Robust Solution for the Supermarket Shelf Audit Problem: Obsolete Price Tags in Shelves Type Conference Article
  Year 2024 Publication Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 26th Iberoamerican Congress on Pattern Recognition, CIARP 2023 Coimbra 27 – 30 November 2023 Abbreviated Journal  
  Volume Vol. 14470 Issue Pages 257–271  
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  ISSN (down) 03029743 ISBN 978-303149017-0 Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial 249  
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Author Xavier Soria, Angel Sappa, Patricio Humanante, Arash Akbarinia pdf  url
doi  openurl
  Title Dense extreme inception network for edge detection. Type Journal Article
  Year 2023 Publication Pattern Recognition Abbreviated Journal  
  Volume Vol. 139 Issue Pages  
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  Series Volume Series Issue Edition  
  ISSN (down) 00313203 ISBN Medium  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 216  
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Author Roberto Jacome Galarza; Miguel-Andrés Realpe-Robalino; Chamba-Eras LuisAntonio; Viñán-Ludeña MarlonSantiago and Sinche-Freire Javier-Francisco pdf  openurl
  Title 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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  Notes Approved no  
  Call Number gtsi @ user @ Serial 97  
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Author Cristhian A. Aguilera; Cristhian Aguilera; Angel D. Sappa pdf  openurl
  Title Melamine faced panels defect classification beyond the visible spectrum. Type Journal Article
  Year 2018 Publication In Sensors 2018 Abbreviated Journal  
  Volume Vol. 11 Issue Issue 11 Pages  
  Keywords  
  Abstract In this work, we explore the use of images from different spectral bands to classify defects in melamine faced panels, which could appear through the production process. Through experimental evaluation, we evaluate the use of images from the visible (VS), near-infrared (NIR), and long wavelength infrared (LWIR), to classify the defects using a feature descriptor learning approach together with a support vector machine classifier. Two descriptors were evaluated, Extended Local Binary Patterns (E-LBP) and SURF using a Bag of Words (BoW) representation. The evaluation was carried on with an image set obtained during this work, which contained five different defect categories that currently occurs in the industry. Results show that using images from beyond

the visual spectrum helps to improve classification performance in contrast with a single visible spectrum solution.
 
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  Area Expedition Conference  
  Notes Approved no  
  Call Number gtsi @ user @ Serial 89  
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