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Author |
Patricia L. Suárez, Dario Carpio, and Angel Sappa |
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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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Call Number |
cidis @ cidis @ |
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162 |
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Author |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla; Riad I. Hammoud |
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Title |
Near InfraRed Imagery Colorization |
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Conference Article |
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2018 |
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25 th IEEE International Conference on Image Processing, ICIP 2018 |
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2237-2241 |
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This paper proposes a stacked conditional Generative
Adversarial Network-based method for Near InfraRed
(NIR) imagery colorization. We propose a variant architecture
of Generative Adversarial Network (GAN) that uses multiple
loss functions over a conditional probabilistic generative model.
We show that this new architecture/loss-function yields better
generalization and representation of the generated colored IR
images. The proposed approach is evaluated on a large test
dataset and compared to recent state of the art methods using
standard metrics.1
Index Terms—Convolutional Neural Networks (CNN), Generative
Adversarial Network (GAN), Infrared Imagery colorization. |
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gtsi @ user @ |
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81 |
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Author |
Xavier Soria; Angel D. Sappa; Arash Akbarinia |
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Title |
Multispectral Single-Sensor RGB-NIR Imaging: New Challenges an Oppotunities |
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2017 |
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The 7th International Conference on Image Processing Theory, Tools and Application |
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1-6 |
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72 |
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Author |
Roberto Jacome Galarza. |
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Title |
Multimodal deep learning for crop yield prediction. |
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2022 |
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Doctoral Symposium on Information and Communication Technologies –DSICT 2022. Octubre 12-14. |
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1647 |
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Communicationsin Computer and Infor |
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106-117 |
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Call Number |
cidis @ cidis @ |
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193 |
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Author |
Miguel Realpe; Boris X. Vintimilla; Ljubo Vlacic |
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Title |
Multi-sensor Fusion Module in a Fault Tolerant Perception System for Autonomous Vehicles |
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2016 |
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Journal of Automation and Control Engineering (JOACE) |
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Vol. 4 |
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pp. 430-436 |
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Fault Tolerance, Data Fusion, Multi-sensor Fusion, Autonomous Vehicles, Perception System |
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Driverless vehicles are currently being tested on public roads in order to examine their ability to perform in a safe and reliable way in real world situations. However, the long-term reliable operation of a vehicle’s diverse sensors and the effects of potential sensor faults in the vehicle system have not been tested yet. This paper is proposing a sensor fusion architecture that minimizes the influence of a sensor fault. Experimental results are presented simulating faults by introducing displacements in the sensor information from the KITTI dataset. |
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English |
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English |
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no |
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Call Number |
cidis @ cidis @ |
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51 |
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Author |
Low S., Inkawhich N., Nina O., Sappa A. and Blasch E. |
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Title |
Multi-modal Aerial View Object Classification Challenge Results-PBVS 2022. |
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Conference Article |
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Year |
2022 |
Publication |
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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417-425 |
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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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cidis @ cidis @ |
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177 |
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Author |
Spencer Low, Oliver Nina, Angel D. Sappa, Erik Blasch, Nathan Inkawhich |
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Title |
Multi-modal Aerial View Object Classification Challenge Results – PBVS 2023 |
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Conference Article |
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Year |
2023 |
Publication |
19th IEEE Workshop on Perception Beyond the Visible Spectrum de la Conferencia Computer Vision & Pattern Recognition CVPR 2023, junio 18-28 |
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cidis @ cidis @ |
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212 |
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Author |
Spencer Low, Oliver Nina, Angel D. Sappa, Erik Blasch, Nathan Inkawhich |
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Title |
Multi-modal Aerial View Image Challenge: Translation from Synthetic Aperture Radar to Electro-Optical Domain Results – PBVS 2023 |
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Conference Article |
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Year |
2023 |
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19th IEEE Workshop on Perception Beyond the Visible Spectrum de la Conferencia Computer Vision & Pattern Recognition CVPR 2023, junio 18-28 |
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cidis @ cidis @ |
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211 |
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Author |
Rafael E. Rivadeneira, A. D. S. and B. X. V. |
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Title |
Multi-Image Super-Resolution for Thermal Images. |
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Conference Article |
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2022 |
Publication |
17th International Conference on Computer Vision Theory and Applications (VISAPP 2022), febrero 6-8 |
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cidis @ cidis @ |
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181 |
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Author |
Mehri, A, Ardakani, P.B., Sappa, A.D. |
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Title |
MPRNet: Multi-Path Residual Network for Lightweight Image Super Resolution. |
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2021 |
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In IEEE Winter Conference on Applications of Computer Vision WACV 2021, enero 5-9, 2021 |
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2703-2712 |
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cidis @ cidis @ |
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148 |
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