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Author  |
Rafael E. Rivadeneira, Angel Domingo Sappa, Vintimilla B. X. and Hammoud R. |

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Title |
A Novel Domain Transfer-Based Approach for Unsupervised Thermal Image Super- Resolution. |
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Journal Article |
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2022 |
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Sensors |
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Sensors |
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Vol. 22 |
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Issue 6 |
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Call Number |
cidis @ cidis @ |
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170 |
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Rafael E. Rivadeneira, Henry O. Velesaca, Angel D. Sappa |

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Title |
Object Detection in Very Low-Resolution Thermal Images through a Guided-Based Super-Resolution Approach |
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Conference Article |
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2023 |
Publication |
17th International Conference On Signal Image Technology & Internet Based System, Bangkok, 8-10 November 2023 |
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311 - 318 |
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cidis @ cidis @ |
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224 |
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Author  |
Rafael E. Rivadeneira; Angel D. Sappa; Boris X. Vintimilla |

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Title |
Thermal Image Super-Resolution: a Novel Architecture and Dataset |
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Conference Article |
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Year |
2020 |
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The 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020); Valletta, Malta; 27-29 Febrero 2020 |
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4 |
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111-119 |
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Thermal images, Far Infrared, Dataset, Super-Resolution. |
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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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978-989758402-2 |
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gtsi @ user @ |
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121 |
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Rafael E. Rivadeneira; Angel D. Sappa; Boris X. Vintimilla; Lin Guo; Jiankun Hou; Armin Mehri; Parichehr Behjati; Ardakani Heena Patel; Vishal Chudasama; Kalpesh Prajapati; Kishor P. Upla; Raghavendra Ramachandra; Kiran Raja; Christoph Busch; Feras Almasri; Olivier Debeir; Sabari Nathan; Priya Kansal; Nolan Gutierrez; Bardia Mojra; William J. Beksi |

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Title |
Thermal Image Super-Resolution Challenge – PBVS 2020 |
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Conference Article |
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Year |
2020 |
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The 16th IEEE Workshop on Perception Beyond the Visible Spectrum on the Conference on Computer Vision and Pattern Recongnition (CVPR 2020) |
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2020-June |
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9151059 |
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432-439 |
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This paper summarizes the top contributions to the first challenge on thermal image super-resolution (TISR) which was organized as part of the Perception Beyond the Visible Spectrum (PBVS) 2020 workshop. In this challenge, a novel thermal image dataset is considered together with stateof-the-art approaches evaluated under a common framework.
The dataset used in the challenge consists of 1021 thermal images, obtained from three distinct thermal cameras at different resolutions (low-resolution, mid-resolution, and high-resolution), resulting in a total of 3063 thermal images. From each resolution, 951 images are used for training and 50 for testing while the 20 remaining images are used for two proposed evaluations. The first evaluation consists of downsampling the low-resolution, midresolution, and high-resolution thermal images by x2, x3 and x4 respectively, and comparing their super-resolution
results with the corresponding ground truth images. The second evaluation is comprised of obtaining the x2 superresolution from a given mid-resolution thermal image and comparing it with the corresponding semi-registered highresolution thermal image. Out of 51 registered participants, 6 teams reached the final validation phase. |
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English |
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21607508 |
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978-172819360-1 |
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no |
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cidis @ cidis @ |
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123 |
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Author  |
Rafael E. Rivadeneira; Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla. |

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Title |
Thermal Image SuperResolution through Deep Convolutional Neural Network. |
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Conference Article |
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2019 |
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16th International Conference on Image Analysis and Recognition (ICIAR 2019); Waterloo, Canadá |
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417-426 |
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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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gtsi @ user @ |
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103 |
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Author  |
Rafael Eduardo Rivadeneira Campodónico |

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Title |
Thermal Image Super-Resolution using Deep Learning Techniques (Ph. D. Angel Sappa, Director & Ph. D. Boris Vintimilla, Codirector.). Ph. D. thesis. |
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Year |
2023 |
Publication |
Ediciones FIEC-ESPOL |
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cidis @ cidis @ |
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262 |
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Rafael Rivadeneira, Henry Velesaca & Angel Sappa |


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Title |
Cross-Spectral Image Registration: a Comparative Study and a New Benchmark Dataset |
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Conference Article |
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2024 |
Publication |
Lecture Notes in Networks and Systems: 4th International Conference on Innovations in Computational Intelligence and Computer Vision (ICICV 2024) |
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Vol. 1117 LNNS |
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1-12 |
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23673370 |
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978-981976991-9 |
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cidis @ cidis @ |
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237 |
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Author  |
Rangnekar,Aneesha; Mulhollan,Zachary; Vodacek,Anthony; Hoffman,Matthew; Sappa,Angel D.; Yu,Jun et al. |

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Title |
Semi-Supervised Hyperspectral Object Detection Challenge Results-PBVS 2022. |
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Conference Article |
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2022 |
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Conference on Computer Vision and Pattern Recognition Workshops, (CVPRW 2022), junio 19-24. |
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CONFERENCE |
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2022-June |
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389-397 |
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cidis @ cidis @ |
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176 |
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Author  |
Rato D., Oliveira M., Santos V., Sappa A. & Raducanu B. |
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Title |
Multi-View 2D to 3D Lifting Video-Based Optimization: A Robust Approach for Human Pose Estimation with Occluded Joint Prediction |
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2024 |
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IEEE Int. Conf. on Intelligent Robots and Systems (IROS), Abu Dhabi, October 14-18, 2024 |
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cidis @ cidis @ |
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255 |
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Author  |
Raul A. Mira; Patricia L. Suarez; Rafael E. Rivadeneira; Angel D. Sappa |

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Title |
PETRA: A Crowdsourcing-Based Platform for Rocks Data Collection and Characterization |
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Conference Article |
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2019 |
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IEEE ETCM 2019 Fourth Ecuador Technical Chapters Meeting; Guayaquil, Ecuador |
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1-6 |
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This paper presents details of a distributed platform intended for data acquisition, evaluation, storage and visualization, which is fully implemented under the crowdsourcing paradigm. The proposed platform is the result from collaboration between computer science and petrology researchers and it is intended for academic purposes. The platform is designed within a MTV (Model, Template and View) architecture and also designed for a collaborative data store and managing of rocks from multiple readers and writers, taking advantage of ubiquity of web applications, and neutrality of researchers from different
communities to validate the data. The platform is being used and validated by students and academics from our university; in the near future it will be open to other users interested on this topic. |
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gtsi @ user @ |
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112 |
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