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| Author | Henry O. Velesaca, Patricia L. Suarez, Dario Carpio, and Angel D. Sappa | ||||
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Synthesized Image Datasets: Towards an Annotation-Free Instance Segmentation Strategy | Type | Conference Article | ||
| Year | 2021 | Publication | 16 International Symposium on Visual Computing. Octubre 4-6, 2021. Lecture Notes in Computer Science | Abbreviated Journal | |
| Volume | 13017 | Issue | Pages | 131-143 | |
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 163 | ||
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| Author | 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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Thermal Image Super-Resolution Challenge – PBVS 2020 | Type | Conference Article | ||
| Year | 2020 | Publication | The 16th IEEE Workshop on Perception Beyond the Visible Spectrum on the Conference on Computer Vision and Pattern Recongnition (CVPR 2020) | Abbreviated Journal | |
| Volume | 2020-June | Issue | 9151059 | Pages | 432-439 |
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| Abstract | 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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| Language | English | Summary Language | Original Title | ||
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| ISSN | 21607508 | ISBN | 978-172819360-1 | Medium | |
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 123 | ||
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| Author | Rivadeneira R.E., Sappa A.D., Vintimilla B.X., Nathan S., Kansal P., Mehri A et al. | ||||
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Thermal Image Super-Resolution Challenge – PBVS 2021. | Type | Conference Article | ||
| Year | 2021 | Publication | In IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021., junio 19 – 25, 2021 | Abbreviated Journal | |
| Volume | Issue | Pages | 4354-4362 | ||
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 151 | ||
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| Author | Rafael E. Rivadeneira, Angel D. Sappa, Boris X. Vintimilla, Chenyang Wang, Junjun Jiang, Xianming Liu, Zhiwei Zhong, Dai Bin, Li Ruodi, Li Shengye | ||||
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Thermal Image Super-Resolution Challenge Results – PBVS 2023 | Type | Conference Article | ||
| Year | 2023 | Publication | 19th IEEE Workshop on Perception Beyond the Visible Spectrum de la Conferencia Computer Vision & Pattern Recognition CVPR 2023, junio 18-28 | Abbreviated Journal | |
| Volume | 2023-June | Issue | Pages | 470 - 478 | |
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| ISSN | 21607508 | ISBN | 979-835030249-3 | Medium | |
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 210 | ||
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| Author | Rafael E. Rivadeneira, Angel D. Sappa, Boris X. Vintimilla, Jin Kim, Dogun Kim et al. | ||||
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Thermal Image Super-Resolution Challenge Results- PBVS 2022. | Type | Conference Article | ||
| Year | 2022 | Publication | Computer Vision and Pattern Recognition Workshops, (CVPRW 2022), junio 19-24. | Abbreviated Journal | CONFERENCE |
| Volume | 2022-June | Issue | Pages | 349-357 | |
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| Abstract | This paper presents results from the third Thermal Image Super-Resolution (TISR) challenge organized in the Perception Beyond the Visible Spectrum (PBVS) 2022 workshop. The challenge uses the same thermal image dataset as the first two challenges, with 951 training images and 50 validation images at each resolution. A set of 20 images was kept aside for testing. The evaluation tasks were to measure the PSNR and SSIM between the SR image and the ground truth (HR thermal noisy image downsampled by four), and also to measure the PSNR and SSIM between the SR image and the semi-registered HR image (acquired with another camera). The results outperformed those from last year’s challenge, improving both evaluation metrics. This year, almost 100 teams participants registered for the challenge, showing the community’s interest in this hot topic. |
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 175 | ||
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| Author | Rafael E. Rivadeneira; Angel D. Sappa; Boris X. Vintimilla | ||||
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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. | ||||
| Title |
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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| Call Number | cidis @ cidis @ | Serial | 179 | ||
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| Author | Rafael E. Rivadeneira; Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla. | ||||
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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. | ||||
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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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| Call Number | cidis @ cidis @ | Serial | 152 | ||
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| Author | Xavier Soria, Yachuan Li, Mohammad Rouhani & Angel D. Sappa | ||||
| Title |
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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