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Author (down) Marjorie Chalen; Boris X. Vintimilla pdf  openurl
  Title Towards Action Prediction Applying Deep Learning Type Journal Article
  Year 2019 Publication Latin American Conference on Computational Intelligence (LA-CCI); Guayaquil, Ecuador; 11-15 Noviembre 2019 Abbreviated Journal  
  Volume Issue Pages pp. 1-3  
  Keywords action prediction, early recognition, early detec- tion, action anticipation, cnn, deep learning, rnn, lstm.  
  Abstract Considering the incremental development future action prediction by video analysis task of computer vision where it is done based upon incomplete action executions. Deep learning is playing an important role in this task framework. Thus, this paper describes recently techniques and pertinent datasets utilized in human action prediction task.  
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  Call Number cidis @ cidis @ Serial 129  
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Author (down) Ma. Paz Velarde; Erika Perugachi; Dennis G. Romero; Ángel D. Sappa; Boris X. Vintimilla pdf  url
openurl 
  Title Análisis del movimiento de las extremidades superiores aplicado a la rehabilitación física de una persona usando técnicas de visión artificial. Type Journal Article
  Year 2015 Publication Revista Tecnológica ESPOL-RTE Abbreviated Journal  
  Volume Vol. 28 Issue Pages pp. 1-7  
  Keywords Rehabilitation; RGB-D Sensor; Computer Vision; Upper limb  
  Abstract Comúnmente durante la rehabilitación física, el diagnóstico dado por el especialista se basa en observaciones cualitativas que sugieren, en algunos casos, conclusiones subjetivas. El presente trabajo propone un enfoque cuantitativo, orientado a servir de ayuda a fisioterapeutas, a través de una herramienta interactiva y de bajo costo que permite medir los movimientos de miembros superiores. Estos movimientos son capturados por un sensor RGB-D y procesados mediante la metodología propuesta, dando como resultado una eficiente representación de movimientos, permitiendo la evaluación cuantitativa de movimientos de los miembros superiores.  
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  Call Number cidis @ cidis @ Serial 39  
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Author (down) Lukas Danev; Marten Hamann; Nicolas Fricke; Tobias Hollarek; Dennys Paillacho pdf  openurl
  Title Development of animated facial expression to express emotions in a robot: RobotIcon. Type Conference Article
  Year 2017 Publication IEEE Ecuador Technical Chapter Meeting (ETCM) Abbreviated Journal  
  Volume 2017-January Issue Pages 1-6  
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  Call Number gtsi @ user @ Serial 71  
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Author (down) Luis Chuquimarca, Renzo Pacheco, Paula Gonzalez, Boris Vintimilla & Sergio Velastin pdf  openurl
  Title Fruit defect detection using CNN models with real and virtual data. Type Conference Article
  Year 2023 Publication Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISIGRAPP 2023 Abbreviated Journal  
  Volume Issue Pages 272 - 279  
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  Call Number cidis @ cidis @ Serial 203  
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Author (down) Luis Chuquimarca, Boris Vintimilla & Sergio Velastin pdf  openurl
  Title Banana Ripeness Level Classification using a Simple CNN Model Trained with Real and Synthetic Datasets. Type Conference Article
  Year 2023 Publication Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISIGRAPP 2023 Abbreviated Journal  
  Volume Issue Pages 536 - 543  
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  Call Number cidis @ cidis @ Serial 202  
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Author (down) Luis C. Herrera, Leslie del R. Lima, Nayeth I. Solorzano, Jonathan S. Paillacho & Dennys Paillacho. url  openurl
  Title Metrics Design of Usability and Behavior Analysis of a Human-Robot-Game Platform. Type Conference Article
  Year 2021 Publication The 2nd International Conference on Applied Technologies (ICAT 2020), diciembre 2-4. Communication in Computer and Information Science Abbreviated Journal  
  Volume 1388 Issue Pages 164-178  
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  Call Number cidis @ cidis @ Serial 191  
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Author (down) Low S., Inkawhich N., Nina O., Sappa A. and Blasch E. pdf  url
openurl 
  Title Multi-modal Aerial View Object Classification Challenge Results-PBVS 2022. Type Conference Article
  Year 2022 Publication Conference on Computer Vision and Pattern Recognition Workshops, (CVPRW 2022), junio 19-24. Abbreviated Journal CONFERENCE  
  Volume 2022-June Issue Pages 417-425  
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  Abstract This paper details the results and main findings of the

second iteration of the Multi-modal Aerial View Object

Classification (MAVOC) challenge. This year’s MAVOC

challenge is the second iteration. The primary goal of

both MAVOC challenges is to inspire research into methods for building recognition models that utilize both synthetic aperture radar (SAR) and electro-optical (EO) input

modalities. Teams are encouraged/challenged to develop

multi-modal approaches that incorporate complementary

information from both domains. While the 2021 challenge

showed a proof of concept that both modalities could be

used together, the 2022 challenge focuses on the detailed

multi-modal models. Using the same UNIfied COincident

Optical and Radar for recognitioN (UNICORN) dataset and

competition format that was used in 2021. Specifically, the

challenge focuses on two techniques, (1) SAR classification

and (2) SAR + EO classification. The bulk of this document is dedicated to discussing the top performing methods

and describing their performance on our blind test set. Notably, all of the top ten teams outperform our baseline. For

SAR classification, the top team showed a 129% improvement over our baseline and an 8% average improvement

from the 2021 winner. The top team for SAR + EO classification shows a 165% improvement with a 32% average

improvement over 2021.		  
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  Call Number cidis @ cidis @ Serial 177  
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Author (down) Juan A. Carvajal; Dennis G. Romero; Angel D. Sappa pdf  openurl
  Title Fine-tuning deep convolutional networks for lepidopterous genus recognition Type Journal Article
  Year 2017 Publication Lecture Notes in Computer Science Abbreviated Journal  
  Volume Vol. 10125 LNCS Issue Pages pp. 467-475  
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  Call Number gtsi @ user @ Serial 63  
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Author (down) José Reyes; Axel Godoy; Miguel Realpe. pdf  openurl
  Title Uso de software de código abierto para fusión de imágenes agrícolas multiespectrales adquiridas con drones. Type Conference Article
  Year 2019 Publication International Multi-Conference of Engineering, Education and Technology (LACCEI 2019); Montego Bay, Jamaica Abbreviated Journal  
  Volume 2019-July Issue Pages  
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  Abstract Los drones o aeronaves no tripuladas son muy útiles para la adquisición de imágenes, de forma mucho más simple que los satélites o aviones. Sin embargo, las imágenes adquiridas por drones deben ser combinadas de alguna forma para convertirse en información de valor sobre un terreno o cultivo. Existen diferentes programas que reciben imágenes y las combinan en una sola imagen, cada uno con diferentes características (rendimiento, precisión, resultados, precio, etc.). En este estudio se revisaron diferentes programas de código abierto para fusión de imágenes, con el ?n de establecer cuál de ellos es más útil, especí?camente para ser utilizado por pequeños y medianos agricultores en Ecuador. Los resultados pueden ser de interés para diseñadores de software, ya que al utilizar código abierto, es posible modi?car e integrar los programas en un ?ujo de trabajo más simpli?cado. Además, que permite disminuir costos debido a que no requiere de pagos de licencias para su uso, lo cual puede repercutir en un mayor acceso a la tecnología para los pequeños y medianos agricultores. Como parte de los resultados de este estudio se ha creado un repositorio de acceso público con algoritmos de pre-procesamiento necesarios para manipular las imágenes adquiridas por una cámara multiespectral y para luego obtener un mapa completo en formatos RGB, CIR y NDVI.  
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  Call Number gtsi @ user @ Serial 102  
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Author (down) Jorge L. Charco; Boris X. Vintimilla; Angel D. Sappa pdf  openurl
  Title Deep learning based camera pose estimation in multi-view environment. Type Conference Article
  Year 2018 Publication 14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018) Abbreviated Journal  
  Volume Issue Pages 224-228  
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  Abstract This paper proposes to use a deep learning network architecture for relative camera pose estimation on a multi-view environment. The proposed network is a variant architecture of AlexNet to use as regressor for prediction the relative translation and rotation as output. The proposed approach is trained from scratch on a large data set that takes as input a pair of images from the same scene. This new architecture is compared with a previous approach using standard metrics, obtaining better results on the relative camera pose.  
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  Notes Approved no  
  Call Number gtsi @ user @ Serial 93  
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