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Author |
Jorge L. Charco; Angel D. Sappa; Boris X. Vintimilla; Henry O. Velesaca |
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Title |
Transfer Learning from Synthetic Data in the Camera Pose Estimation Problem |
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Conference Article |
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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 |
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4 |
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498-505 |
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Keywords |
Relative Camera Pose Estimation, Siamese Architecture, Synthetic Data, Deep Learning, Multi-View Environments, Extrinsic Camera Parameters. |
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This paper presents a novel Siamese network architecture, as a variant of Resnet-50, to estimate the relative camera pose on multi-view environments. In order to improve the performance of the proposed model
a transfer learning strategy, based on synthetic images obtained from a virtual-world, is considered. The
transfer learning consist of first training the network using pairs of images from the virtual-world scenario
considering different conditions (i.e., weather, illumination, objects, buildings, etc.); then, the learned weight
of the network are transferred to the real case, where images from real-world scenarios are considered. Experimental results and comparisons with the state of the art show both, improvements on the relative pose
estimation accuracy using the proposed model, as well as further improvements when the transfer learning
strategy (synthetic-world data – transfer learning – real-world data) is considered to tackle the limitation on
the training due to the reduced number of pairs of real-images on most of the public data sets. |
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978-989758402-2 |
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gtsi @ user @ |
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120 |
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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 |
Publication |
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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Keywords |
Thermal images, Far Infrared, Dataset, Super-Resolution. |
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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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978-989758402-2 |
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gtsi @ user @ |
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121 |
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Author |
Rafael E. Rivadeneira, Angel D. Sappa and Boris X. Vintimilla |
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Title |
Multi-Image Super-Resolution for Thermal Images. |
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Conference Article |
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Year |
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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181 |
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Author |
Jorge L. Charco, Angel D. Sappa, Boris X. Vintimilla |
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Title |
Human Pose Estimation through A Novel Multi-View Scheme |
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Conference Article |
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Year |
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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5 |
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Pages |
855-862 |
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Keywords |
Multi-View Scheme, Human Pose Estimation, Relative Camera Pose, Monocular Approach |
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Abstract |
This paper presents a multi-view scheme to tackle the challenging problem of the self-occlusion in human
pose estimation problem. The proposed approach first obtains the human body joints of a set of images,
which are captured from different views at the same time. Then, it enhances the obtained joints by using a
multi-view scheme. Basically, the joints from a given view are used to enhance poorly estimated joints from
another view, especially intended to tackle the self occlusions cases. A network architecture initially proposed
for the monocular case is adapted to be used in the proposed multi-view scheme. Experimental results and
comparisons with the state-of-the-art approaches on Human3.6m dataset are presented showing improvements
in the accuracy of body joints estimations. |
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yes |
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cidis @ cidis @ |
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169 |
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Author |
Patricia L. Suárez, Angel D. Sappa and Boris X. Vintimilla |
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Title |
Deep learning-based vegetation index estimation |
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Book Chapter |
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Year |
2021 |
Publication |
Generative Adversarial Networks for Image-to-Image Translation Book. |
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Chapter 9 |
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Issue 2 |
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205-232 |
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no |
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cidis @ cidis @ |
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137 |
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Author |
Rubio, G.A., Agila, W.E |
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Title |
A fuzzy model to manage water in polymer electrolyte membrane fuel cells |
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Journal Article |
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Year |
2021 |
Publication |
In Processes Journal. (Article number 904) |
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Vol. 9 |
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Issue 6 |
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Keywords |
PEM fuel cell, fuzzy, neural network, electrical response, flooding, drying. |
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In this paper, a fuzzy model is presented to determine in real-time the degree of dehydration or flooding of a proton exchange membrane of a fuel cell, to optimize its electrical response and consequently, its autonomous operation. By applying load, current and flux variations in the dry, normal, and flooded states of the membrane, it was determined that the temporal evolution of the fuel cell voltage is characterized by changes in slope and by its voltage oscillations. The results were validated using electrochemical impedance spectroscopy and show slope changes from 0.435 to 0.52 and oscillations from 3.6 mV to 5.2 mV in the dry state, and slope changes from 0.2 to 0.3 and oscillations from 1 mV to 2 mV in the flooded state. The use of fuzzy logic is a novelty and constitutes a step towards the progressive automation of the supervision, perception, and intelligent control of fuel cells, allowing them to reduce their risks and increase their economic benefits. |
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English |
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cidis @ cidis @ |
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153 |
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Author |
Patricia Súarez, Henry Velesaca, Dario Carpio & Angel Sappa |
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Title |
Corn Kernel Classification From Few Training Samples |
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Journal Article |
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Year |
2023 |
Publication |
In journal Artificial Intelligence in Agriculture |
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Vol. 9 |
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pp. 89-99 |
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25897217 |
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cidis @ cidis @ |
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223 |
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Author |
Michael Teutsch, Angel Sappa & Riad Hammoud |
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Title |
Computer Vision in the Infrared Spectrum: Challenges and ApproachesComputer Vision in the Infrared Spectrum: Challenges and Approaches |
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Journal Article |
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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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cidis @ cidis @ |
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166 |
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Author |
Xavier Soria , Gonzalo Pomboza-Junez & Angel Sappa. |
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Title |
LDC: Lightweight Dense CNN for Edge Detection. |
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Journal Article |
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2022 |
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IEEE Access journal |
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Vol. 10 |
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pp. 68281-68290 |
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cidis @ cidis @ |
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183 |
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Author |
Cristhian A. Aguilera; Cristhian Aguilera; Angel D. Sappa |
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Title |
Melamine faced panels defect classification beyond the visible spectrum. |
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Journal Article |
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Year |
2018 |
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In Sensors 2018 |
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Vol. 11 |
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Issue 11 |
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In this work, we explore the use of images from different spectral bands to classify defects in melamine faced panels, which could appear through the production process. Through experimental evaluation, we evaluate the use of images from the visible (VS), near-infrared (NIR), and long wavelength infrared (LWIR), to classify the defects using a feature descriptor learning approach together with a support vector machine classifier. Two descriptors were evaluated, Extended Local Binary Patterns (E-LBP) and SURF using a Bag of Words (BoW) representation. The evaluation was carried on with an image set obtained during this work, which contained five different defect categories that currently occurs in the industry. Results show that using images from beyond
the visual spectrum helps to improve classification performance in contrast with a single visible spectrum solution. |
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gtsi @ user @ |
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89 |
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