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| Author | Rafael E. Rivadeneira, Angel D. Sappa and Boris X. Vintimilla | ||||
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Multi-Image Super-Resolution for Thermal Images. | Type | Conference Article | ||
| Year | 2022 | Publication | Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISIGRAPP 2022 | Abbreviated Journal | |
| Volume | 4 | Issue | Pages | 635 - 642 | |
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 181 | ||
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| Author | Low S., Inkawhich N., Nina O., Sappa A. and Blasch E. | ||||
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Multi-modal Aerial View Object Classification Challenge Results-PBVS 2022. | Type | Conference Article | ||
| Year | 2022 | Publication | Conference on Computer Vision and Pattern Recognition Workshops, (CVPRW 2022), junio 19-24. | Abbreviated Journal | CONFERENCE |
| Volume | 2022-June | Issue | Pages | 417-425 | |
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| Abstract | This paper details the results and main findings of the second iteration of the Multi-modal Aerial View Object Classification (MAVOC) challenge. This year’s MAVOC challenge is the second iteration. The primary goal of both MAVOC challenges is to inspire research into methods for building recognition models that utilize both synthetic aperture radar (SAR) and electro-optical (EO) input modalities. Teams are encouraged/challenged to develop multi-modal approaches that incorporate complementary information from both domains. While the 2021 challenge showed a proof of concept that both modalities could be used together, the 2022 challenge focuses on the detailed multi-modal models. Using the same UNIfied COincident Optical and Radar for recognitioN (UNICORN) dataset and competition format that was used in 2021. Specifically, the challenge focuses on two techniques, (1) SAR classification and (2) SAR + EO classification. The bulk of this document is dedicated to discussing the top performing methods and describing their performance on our blind test set. Notably, all of the top ten teams outperform our baseline. For SAR classification, the top team showed a 129% improvement over our baseline and an 8% average improvement from the 2021 winner. The top team for SAR + EO classification shows a 165% improvement with a 32% average improvement over 2021. |
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 177 | ||
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| Author | Roberto Jacome Galarza. | ||||
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Multimodal deep learning for crop yield prediction. | Type | Conference Article | ||
| Year | 2022 | Publication | Doctoral Symposium on Information and Communication Technologies –DSICT 2022. Octubre 12-14. | Abbreviated Journal | |
| Volume | 1647 | Issue | Communicationsin Computer and Infor | Pages | 106-117 |
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| Call Number | cidis @ cidis @ | Serial | 193 | ||
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| Author | Benítez-Quintero J., Quevedo-Pinos O., Calderon, Fernanda | ||||
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Notes on Sulfur Fluxes in Urban Areas with Industrial Activity | Type | Conference Article | ||
| Year | 2022 | Publication | 20th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology, LACCEI 2022, | Abbreviated Journal | |
| Volume | 2022-July | Issue | Pages | ||
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| Call Number | cidis @ cidis @ | Serial | 201 | ||
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| Author | Velesaca, Henry O.; Suárez, Patricia L.; Sappa, Angel D.; Carpio, Dario; Rivadeneira, Rafael E.; Sanchez, Angel | ||||
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Review on Common Techniques for Urban Environment Video Analytics. | Type | Conference Article | ||
| Year | 2022 | Publication | WORKSHOP BRASILEIRO DE CIDADES INTELIGENTES (WBCI 2022) | Abbreviated Journal | |
| Volume | Issue | Porto Alegre: Sociedade Brasileira de Computação | Pages | 107-118 | |
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| Call Number | cidis @ cidis @ | Serial | 192 | ||
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| Author | Rangnekar,Aneesha; Mulhollan,Zachary; Vodacek,Anthony; Hoffman,Matthew; Sappa,Angel D.; Yu,Jun et al. | ||||
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Semi-Supervised Hyperspectral Object Detection Challenge Results-PBVS 2022. | Type | Conference Article | ||
| Year | 2022 | Publication | Conference on Computer Vision and Pattern Recognition Workshops, (CVPRW 2022), junio 19-24. | Abbreviated Journal | CONFERENCE |
| Volume | 2022-June | Issue | Pages | 389-397 | |
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| Call Number | cidis @ cidis @ | Serial | 176 | ||
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| Author | Rafael E. Rivadeneira, Angel D. Sappa, Boris X. Vintimilla, Jin Kim, Dogun Kim et al. | ||||
| Title |
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 | 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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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 179 | ||
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| Author | Jacome-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 | 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 | Silva Steven, Paillacho Dennys, Verdezoto Nervo, Hernandez Juan David | ||||
| Title |
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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