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Author Jorge L. Charco; Angel D. Sappa; Boris X. Vintimilla; Henry O. Velesaca pdf  isbn
openurl 
  Title (down) Transfer Learning from Synthetic Data in the Camera Pose Estimation Problem Type Conference Article
  Year 2020 Publication The 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020); Valletta, Malta; 27-29 Febrero 2020 Abbreviated Journal  
  Volume 4 Issue Pages 498-505  
  Keywords Relative Camera Pose Estimation, Siamese Architecture, Synthetic Data, Deep Learning, Multi-View Environments, Extrinsic Camera Parameters.  
  Abstract This paper presents a novel Siamese network architecture, as a variant of Resnet-50, to estimate the relative camera pose on multi-view environments. In order to improve the performance of the proposed model

a transfer learning strategy, based on synthetic images obtained from a virtual-world, is considered. The

transfer learning consist of first training the network using pairs of images from the virtual-world scenario

considering different conditions (i.e., weather, illumination, objects, buildings, etc.); then, the learned weight

of the network are transferred to the real case, where images from real-world scenarios are considered. Experimental results and comparisons with the state of the art show both, improvements on the relative pose

estimation accuracy using the proposed model, as well as further improvements when the transfer learning

strategy (synthetic-world data – transfer learning – real-world data) is considered to tackle the limitation on

the training due to the reduced number of pairs of real-images on most of the public data sets.		  
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  ISSN ISBN 978-989758402-2 Medium  
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  Call Number gtsi @ user @ Serial 120  
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Author Silva Steven, Paillacho Dennys, Verdezoto Nervo, Hernandez Juan David url  openurl
  Title (down) TOWARDS ONLINE SOCIALLY ACCEPTABLE ROBOT NAVIGATION Type Conference Article
  Year 2022 Publication IEEE INTERNATIONAL CONFERENCE ON AUTOMATION SCIENCE AND ENGINEERING, Abbreviated Journal  
  Volume 2022-August Issue Pages 707-714  
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  Call Number cidis @ cidis @ Serial 199  
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Author Miguel Realpe; Boris X. Vintimilla; L. Vlacic pdf  openurl
  Title (down) Towards Fault Tolerant Perception for autonomous vehicles: Local Fusion. Type Conference Article
  Year 2015 Publication IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM), Siem Reap, 2015. Abbreviated Journal  
  Volume Issue Pages 253-258  
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  Abstract Many robust sensor fusion strategies have been developed in order to reliably detect the surrounding environments of an autonomous vehicle. However, in real situations there is always the possibility that sensors or other components may fail. Thus, internal modules and sensors need to be monitored to ensure their proper function. This paper introduces a general view of a perception architecture designed to detect and classify obstacles in an autonomous vehicle's environment using a fault tolerant framework, whereas elaborates the object detection and local fusion modules proposed in order to achieve the modularity and real-time process required by the system.  
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  Call Number cidis @ cidis @ Serial 37  
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Author Marjorie Chalen; Boris X. Vintimilla pdf  openurl
  Title (down) 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 Emmanuel Moran Barreiro & Boris Vintimilla url  openurl
  Title (down) Towards a Robust Solution for the Supermarket Shelf Audit Problem: Obsolete Price Tags in Shelves Type Conference Article
  Year 2023 Publication Lecture Notes in Computer Science. 26th Iberoamerican Congress on Pattern Recognition Abbreviated Journal  
  Volume 14469 LNCS Issue Pages 257 - 271  
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  Call Number cidis @ cidis @ Serial 222  
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Author Emmanuel Moran, Boris Vintimilla & Miguel Realpe openurl 
  Title (down) Towards a Robust Solution for the Supermarket Shelf Audit Problem. 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 912 - 919  
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  Call Number cidis @ cidis @ Serial 204  
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Author Patricia Suarez & Angel Sappa pdf  openurl
  Title (down) Toward a thermal image-like representation 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 133 - 140  
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  Call Number cidis @ cidis @ Serial 205  
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Author Armin Mehri; Parichehr Behjati; Angel Domingo Sappa pdf  openurl
  Title (down) TnTViT-G: Transformer in Transformer Network for Guidance Super Resolution. Type Journal Article
  Year 2023 Publication IEEE Access Abbreviated Journal  
  Volume Vol. 11 Issue Pages pp. 11529-11540  
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  ISSN 21693536 ISBN Medium  
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  Call Number cidis @ cidis @ Serial 207  
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Author Xavier Soria, Yachuan Li, Mohammad Rouhani & Angel D. Sappa pdf  openurl
  Title (down) Tiny and Efficient Model for the Edge Detection Generalization Type Conference Article
  Year 2023 Publication Proceedings – 2023 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2023 Abbreviated Journal  
  Volume Issue Pages 1356 - 1365  
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  Call Number cidis @ cidis @ Serial 229  
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Author Jacome-Galarza L.-R., Realpe Robalino M.-A., Paillacho Corredores J., Benavides Maldonado J.-L. url  openurl
  Title (down) Time series in sensor data using state of the art deep learning approaches: A systematic literature review. Type Conference Article
  Year 2022 Publication VII International Conference on Science, Technology and Innovation for Society (CITIS 2021), mayo 26-28.  Smart Innovation, Systems and Technologies. Abbreviated Journal  
  Volume 252 Issue Pages 503-514  
  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.		  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 152  
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