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Author Rafael E. Rivadeneira; Angel D. Sappa; Boris X. Vintimilla pdf  isbn
openurl 
  Title (up) Thermal Image Super-Resolution: a Novel Architecture and Dataset 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 111-119  
  Keywords Thermal images, Far Infrared, Dataset, Super-Resolution.  
  Abstract This paper proposes a novel CycleGAN architecture for thermal image super-resolution, together with a large

dataset consisting of thermal images at different resolutions. The dataset has been acquired using three thermal

cameras at different resolutions, which acquire images from the same scenario at the same time. The thermal

cameras are mounted in rig trying to minimize the baseline distance to make easier the registration problem.

The proposed architecture is based on ResNet6 as a Generator and PatchGAN as Discriminator. The novelty

on the proposed unsupervised super-resolution training (CycleGAN) is possible due to the existence of aforementioned thermal images—images of the same scenario with different resolutions. The proposed approach

is evaluated in the dataset and compared with classical bicubic interpolation. The dataset and the network are

available.
 
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  ISSN ISBN 978-989758402-2 Medium  
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  Notes Approved no  
  Call Number gtsi @ user @ Serial 121  
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Author Rivadeneira, Rafael E.; Sappa, Angel D. and Vintimilla Boris X. url  openurl
  Title (up) Thermal Image Super-Resolution: A Novel Unsupervised Approach. Type Book Chapter
  Year 2022 Publication Communications in Computer and Information Science, 15th International Communications in Computer and Information Science Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications Abbreviated Journal BOOK  
  Volume 1474 Issue Pages 495-506  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 179  
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Author Rafael E. Rivadeneira; Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla. pdf  openurl
  Title (up) Thermal Image SuperResolution through Deep Convolutional Neural Network. Type Conference Article
  Year 2019 Publication 16th International Conference on Image Analysis and Recognition (ICIAR 2019); Waterloo, Canadá Abbreviated Journal  
  Volume Issue Pages 417-426  
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  Abstract Due to the lack of thermal image datasets, a new dataset has been acquired for proposed a superesolution approach using a Deep Convolution Neural Network schema. In order to achieve this image enhancement process a new thermal images dataset is used. Di?erent experiments have been carried out, ?rstly, the proposed architecture has been trained using only images of the visible spectrum, and later it has been trained with images of the thermal spectrum, the results showed that with the network trained with thermal images, better results are obtained in the process of enhancing the images, maintaining the image details and perspective. The thermal dataset is available at http://www.cidis.espol.edu.ec/es/dataset  
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  Notes Approved no  
  Call Number gtsi @ user @ Serial 103  
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Author Jacome-Galarza L.-R., Realpe Robalino M.-A., Paillacho Corredores J., Benavides Maldonado J.-L. url  openurl
  Title (up) 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 Vol. 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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  Call Number cidis @ cidis @ Serial 152  
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Author Xavier Soria, Yachuan Li, Mohammad Rouhani & Angel D. Sappa pdf  openurl
  Title (up) 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 Armin Mehri; Parichehr Behjati; Angel Domingo Sappa pdf  openurl
  Title (up) 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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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 207  
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Author Patricia Suarez & Angel Sappa pdf  openurl
  Title (up) 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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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 205  
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Author Emmanuel Moran, Boris Vintimilla & Miguel Realpe openurl 
  Title (up) 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 Emmanuel Moran Barreiro & Boris Vintimilla pdf  url
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  Title (up) 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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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 222  
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Author Marjorie Chalen; Boris X. Vintimilla pdf  openurl
  Title (up) 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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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 129  
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