|   | 
Details
   web
Records
Author (up) Charco, J.L., Sappa, A.D., Vintimilla, B.X., Velesaca, H.O.
Title Camera pose estimation in multi-view environments:from virtual scenarios to the real world Type Journal Article
Year 2021 Publication In Image and Vision Computing Journal. (Vol. 110. Article number 104182) Abbreviated Journal
Volume Issue Pages
Keywords Relative camera pose estimation, Domain adaptation, Siamese architecture, Synthetic data, Multi-view environments
Abstract This paper presents a domain adaptation strategy to efficiently train network architectures for estimating the relative camera pose in multi-view scenarios. The network architectures are fed by a pair of simultaneously acquired

images, hence in order to improve the accuracy of the solutions, and due to the lack of large datasets with pairs of

overlapped images, a domain adaptation strategy is proposed. The domain adaptation strategy consists on transferring the knowledge learned from synthetic images to real-world scenarios. For this, the networks are firstly

trained using pairs of synthetic images, which are captured at the same time by a pair of cameras in a virtual environment; and then, the learned weights of the networks are transferred to the real-world case, where the networks are retrained with a few real images. Different virtual 3D scenarios are generated to evaluate the

relationship between the accuracy on the result and the similarity between virtual and real scenarios—similarity

on both geometry of the objects contained in the scene as well as relative pose between camera and objects in the

scene. Experimental results and comparisons are provided showing that the accuracy of all the evaluated networks for estimating the camera pose improves when the proposed domain adaptation strategy is used,

highlighting the importance on the similarity between virtual-real scenarios.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number cidis @ cidis @ Serial 147
Permanent link to this record
 

 
Author (up) Dennys Paillacho; Nayeth I. Solorzano Alcivar; Jonathan S. Paillacho Corredores
Title LOLY 1.0: A Proposed Human-Robot-Game Platform Architecture for the Engagement of Children with Autism in the Learning Process Type Book Chapter
Year 2021 Publication The international Conference on Systems and Information Sciences (ICCIS 2020), julio 27-29. Advances in Intelligent Systems and Computing. Abbreviated Journal
Volume 1273 Issue Pages 225-238
Keywords
Abstract
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 21945357 ISBN 978-303059193-9 Medium
Area Expedition Conference
Notes Approved no
Call Number cidis @ cidis @ Serial 130
Permanent link to this record
 

 
Author (up) Henry O. Velesaca, Patricia L. Suarez, Dario Carpio, and Angel D. Sappa
Title Synthesized Image Datasets: Towards an Annotation-Free Instance Segmentation Strategy Type Conference Article
Year 2021 Publication In Accepted in 16 International Symposium on Visual Computing. Octubre 4-6, 2021. Lecture Notes in Computer Science (Vol. 13018, pp. 178-190) Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number cidis @ cidis @ Serial 163
Permanent link to this record
 

 
Author (up) Jacome-Galarza L.-R
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 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.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Español Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number cidis @ cidis @ Serial 150
Permanent link to this record
 

 
Author (up) Jácome-Galarza L.-R., Realpe Robalino M.-A., Paillacho Corredores J., Benavides Maldonado J.-L.
Title Time series in sensor data using state of the art deep learning approaches: A systematic literature review. Type Conference Article
Year 2021 Publication VII International Conference on Science, Technology and Innovation for Society (CITIS 2021), mayo 26-28.  Smart Innovation, Systems and Technologies. (Vol. 252, pp. 503-514) Abbreviated Journal
Volume Issue Pages
Keywords time series, deep learning, recurrent networks, sensor data, IoT.
Abstract IoT (Internet of Things) and AI (Artificial Intelligence) are becoming

support tools for several current technological solutions due to significant advancements of these areas. The development of the IoT in various technological fields has contributed to predicting the behavior of various systems such as mechanical, electronic, and control using sensor networks. On the other hand, deep learning architectures have achieved excellent results in complex tasks, where patterns have been extracted in time series. This study has reviewed the most efficient deep learning architectures for forecasting and obtaining trends over time, together with data produced by IoT sensors. In this way, it is proposed to contribute to applications in fields in which IoT is contributing a technological advance such as smart cities, industry 4.0, sustainable agriculture, or robotics. Among the architectures studied in this article related to the process of time series data we have: LSTM (Long Short-Term Memory) for its high precision in prediction and the ability to automatically process input sequences; CNN (Convolutional Neural Networks) mainly in human activity

recognition; hybrid architectures in which there is a convolutional layer for data pre-processing and RNN (Recurrent Neural Networks) for data fusion from different sensors and their subsequent classification; and stacked LSTM Autoencoders that extract the variables from time series in an unsupervised way without the need of manual data pre-processing.Finally, well-known technologies in natural language processing are also used in time series data prediction, such as the attention mechanism and embeddings obtaining promising results.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number cidis @ cidis @ Serial 152
Permanent link to this record
 

 
Author (up) Luis C. Herrera, Leslie del R. Lima, Nayeth I. Solorzano, Jonathan S. Paillacho & Dennys Paillacho.
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
Keywords
Abstract
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number cidis @ cidis @ Serial 135
Permanent link to this record
 

 
Author (up) Luis Jacome-Galarza, Monica Villavicencio-Cabezas, Miguel Realpe-Robalino, Jose Benavides-Maldonado
Title Software Engineering and Distributed Computing in image processing intelligent systems: a systematic literature review. Type Conference Article
Year 2021 Publication 19th LACCEI International Multi-Conference for Engineering, Education, and Technology Abbreviated Journal
Volume Issue Pages
Keywords processing, software engineering, deep learning, intelligent vision systems, cloud computing.
Abstract Deep learning is experiencing an upward technology trend that is revolutionizing intelligent systems in several domains, such as image and speech recognition, machine translation, social network filtering, and the like. By reviewing a total of 80 studies reported from 2016 to 2020, the present article evaluates the application of software engineering to the field

of intelligent image processing systems, it also offers insights about aspects related to distributed computing for this type of systems. Results indicate that several topics of software engineering are mostly applied when academics are involved in developing projects associated to this kind of intelligent systems. The findings provide evidences that Apache Spark is the most

utilized distributed computing framework for image processing. In addition, Tensorflow is a popular framework used to build convolutional neural networks, which are the prevailing deep learning algorithms used in intelligent image processing systems.

Also, among big cloud providers, Amazon Web Services is the preferred computing platform across the industry sectors, followed by Google cloud.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number cidis @ cidis @ Serial 154
Permanent link to this record
 

 
Author (up) Mehri, A, Ardakani, P.B., Sappa, A.D.
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 Issue Pages 7196-7202
Keywords
Abstract
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number cidis @ cidis @ Serial 149
Permanent link to this record
 

 
Author (up) Mehri, A, Ardakani, P.B., Sappa, A.D.
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 Issue Pages 2703-2712
Keywords
Abstract
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number cidis @ cidis @ Serial 148
Permanent link to this record
 

 
Author (up) Michael Teutsch, Angel Sappa & Riad Hammoud
Title Computer Vision in the Infrared Spectrum: Challenges and ApproachesComputer Vision in the Infrared Spectrum: Challenges and Approaches Type Journal Article
Year 2021 Publication Synthesis Lectures on Computer Vision. Volumen 10, No. 2 Pages 138 Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number cidis @ cidis @ Serial 166
Permanent link to this record