| Records |
| Author |
Patricia L. Suárez, Angel D. Sappa, Boris X. Vintimilla |
| Title |
Cycle generative adversarial network: towards a low-cost vegetation index estimation |
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Conference Article |
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2021 |
Publication |
IEEE International Conference on Image Processing (ICIP 2021) |
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2021-September |
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2783-2787 |
| Keywords |
CyclicGAN, NDVI, near infrared spectra, instance normalization. |
| Abstract |
This paper presents a novel unsupervised approach to estimate the Normalized Difference Vegetation Index (NDVI).The NDVI is obtained as the ratio between information from the visible and near infrared spectral bands; in the current work, the NDVI is estimated just from an image of the visible spectrum through a Cyclic Generative Adversarial Network (CyclicGAN). This unsupervised architecture learns to estimate the NDVI index by means of an image translation between the red channel of a given RGB image and the NDVI unpaired index’s image. The translation is obtained by means of a ResNET architecture and a multiple loss function. Experimental results obtained with this unsupervised scheme show the validity of the implemented model. Additionally, comparisons with the state of the art approaches are provided showing improvements with the proposed approach. |
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cidis @ cidis @ |
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164 |
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| Author |
Michael Teutsch, Angel Sappa & Riad Hammoud |
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Computer Vision in the Infrared Spectrum: Challenges and ApproachesComputer Vision in the Infrared Spectrum: Challenges and Approaches |
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2021 |
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Synthesis Lectures on Computer Vision |
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Vol. 10 No. 2 |
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pp. 138 |
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166 |
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Santos, V., Sappa, A.D., Oliveira, M. & de la Escalera, A. |
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Editorial: Special Issue on Autonomous Driving and Driver Assistance Systems – Some Main Trends |
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2021 |
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In Journal: Robotics and Autonomous Systems. (Article number 103832) |
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Vol. 144 |
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158 |
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Velesaca, H.O., Suárez, P. L., Mira, R., & Sappa, A.D. |
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Computer Vision based Food Grain Classification: a Comprehensive Survey |
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2021 |
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In Computers and Electronics in Agriculture Journal. (Article number 106287) |
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Vol. 187 |
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159 |
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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 |
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Conference Article |
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2021 |
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16 International Symposium on Visual Computing. Octubre 4-6, 2021. Lecture Notes in Computer Science |
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13017 |
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131-143 |
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163 |
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| Author |
Patricia L. Suárez, Dario Carpio, and Angel Sappa |
| Title |
Non-Homogeneous Haze Removal through a Multiple Attention Module Architecture. |
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2021 |
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16 International Symposium on Visual Computing. Octubre 4-6, 2021. Lecture Notes in Computer Science |
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13018 |
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178-190 |
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162 |
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Pereira J., Mora M. & W. Agila |
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Qualitative Model to Maximize Shrimp Growth at Low Cost |
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2021 |
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5th Ecuador Technical Chapters Meeting (ETCM 2021), Octubre 12 – 15 |
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cidis @ cidis @ |
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167 |
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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. |
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Conference Article |
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2022 |
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VII International Conference on Science, Technology and Innovation for Society (CITIS 2021), mayo 26-28. Smart Innovation, Systems and Technologies. |
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Vol. 252 |
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503-514 |
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time series, deep learning, recurrent networks, sensor data, IoT. |
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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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cidis @ cidis @ |
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152 |
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| Author |
Rafael E. Rivadeneira, Angel Domingo Sappa, Vintimilla B. X. and Hammoud R. |
| Title |
A Novel Domain Transfer-Based Approach for Unsupervised Thermal Image Super- Resolution. |
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Journal Article |
| Year |
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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cidis @ cidis @ |
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170 |
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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. |
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Conference Article |
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2022 |
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Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISIGRAPP 2022 |
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4 |
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635 - 642 |
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cidis @ cidis @ |
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