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Author |
Jácome Galarza, Luis Roberto |
Title |
Estimation of Corn Crop Yield using Multimodal Deep Learning from Multispectral Images and Environmental Sensors |
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Conference Article |
Year |
2024 |
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19ª Conferência Ibérica de Sistemas e Tecnologias de Informação; CISTI'2024 |
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no |
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cidis @ cidis @ |
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246 |
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Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla |
Title |
Cross-spectral Image Patch Similarity using Convolutional Neural Network |
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Conference Article |
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2017 |
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2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics (ECMSM) |
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no |
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cidis @ cidis @ |
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57 |
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Author |
Angel J. Valencia; Roger M. Idrovo; Angel D. Sappa; Douglas Plaza G.; Daniel Ochoa |
Title |
A 3D Vision Based Approach for Optimal Grasp of Vacuum Grippers |
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Conference Article |
Year |
2017 |
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2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics (ECMSM) |
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1-6 |
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no |
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cidis @ cidis @ |
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60 |
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Author |
Sianna Puente; Cindy Madrid; Miguel Realpe; Boris X. Vintimilla |
Title |
An Empirical Comparison of DCNN libraries to implement the Vision Module of a Danger Management System |
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Conference Article |
Year |
2017 |
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2017 International Conference on Deep Learning Technologies (ICDLT 2017) |
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Part F128535 |
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60-65 |
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no |
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cidis @ cidis @ |
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56 |
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Author |
Xavier Soria; Edgar Riba; Angel D. Sappa |
Title |
Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection |
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Conference Article |
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2020 |
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2020 IEEE Winter Conference on Applications of Computer Vision (WACV) |
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9093290 |
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1912-1921 |
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This paper proposes a Deep Learning based edge de- tector, which is inspired on both HED (Holistically-Nested Edge Detection) and Xception networks. The proposed ap- proach generates thin edge-maps that are plausible for hu- man eyes; it can be used in any edge detection task without previous training or fine tuning process. As a second contri- bution, a large dataset with carefully annotated edges, has been generated. This dataset has been used for training the proposed approach as well the state-of-the-art algorithms for comparisons. Quantitative and qualitative evaluations have been performed on different benchmarks showing im- provements with the proposed method when F-measure of ODS and OIS are considered. |
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978-172816553-0 |
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cidis @ cidis @ |
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126 |
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Angel D. Sappa, Spencer Low, Oliver Nina, Erik Blasch, Dylan Bowald & Nathan Inkawhich |
Title |
Multi-modal Aerial View Image Challenge: SAR Classification |
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Conference Article |
Year |
2024 |
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20th IEEE Workshop on Perception Beyond the Visible Spectrum of the 2024 Conference on Computer Vision and Pattern Recognition |
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cidis @ cidis @ |
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234 |
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Author |
Angel D. Sappa, Spencer Low, Oliver Nina, Erik Blasch, Dylan Bowald & Nathan Inkawhich |
Title |
Multi-modal Aerial View Image Challenge: Sensor Domain Translation |
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Conference Article |
Year |
2024 |
Publication |
20th IEEE Workshop on Perception Beyond the Visible Spectrum of the 2024 Conference on Computer Vision and Pattern Recognition |
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cidis @ cidis @ |
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235 |
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Rafael E. Rivadeneira, Angel D. Sappa, Chenyang Wang, Junjun Jiang, Zhiwei Zhong, Peilin Chen & Shiqi Wang |
Title |
Thermal Image Super Resolution Challenge Results – PBVS 2024 |
Type |
Conference Article |
Year |
2024 |
Publication |
20th IEEE Workshop on Perception Beyond the Visible Spectrum of the 2024 Conference on Computer Vision and Pattern Recognition |
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cidis @ cidis @ |
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233 |
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Author |
Benítez-Quintero J., Quevedo-Pinos O., Calderon, Fernanda |
Title |
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, |
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2022-July |
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cidis @ cidis @ |
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201 |
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Author |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla; Riad I. Hammoud |
Title |
Near InfraRed Imagery Colorization |
Type |
Conference Article |
Year |
2018 |
Publication |
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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