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Author |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla |
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Title |
Cross-spectral Image Patch Similarity using Convolutional Neural Network |
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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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1-5 |
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Call Number |
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 |
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Title |
A 3D Vision Based Approach for Optimal Grasp of Vacuum Grippers |
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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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1-6 |
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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 |
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Title |
An Empirical Comparison of DCNN libraries to implement the Vision Module of a Danger Management System |
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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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cidis @ cidis @ |
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56 |
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Author |
Xavier Soria; Edgar Riba; Angel D. Sappa |
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Title |
Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection |
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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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Author |
Patricia Suarez & Angel D. Sappa |
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Title |
Synthetic Thermal Image Generation from Multi-Cue Input Data |
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2025 |
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20th International Conference on Computer Vision Theory and Applications VISIGRAPP 2025 Porto 26-28 Febrero 2025 |
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Vol. 3 |
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275 - 282 |
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cidis @ cidis @ |
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265 |
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Author |
Gisel Bastidas G., Patricio Moreno V., Boris Vintimilla & Angel D. Sappa |
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Title |
Application-Guided Image Fusion: A Path to Improve Results in High-Level Vision Tasks |
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Conference Article |
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2025 |
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20th International Conference on Computer Vision Theory and Applications VISIGRAPP 2025 Porto 26-28 Febrero 2025 |
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Vol. 3 |
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178 - 187 |
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cidis @ cidis @ |
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266 |
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Author |
Heny O. Velesaca, Andrea Mero, Anjali Singh, Angel Sappa |
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Title |
The Hidden Cost of Poor Annotations: How Label Quality Affects Camouflaged Object Detection Performance |
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Conference Article |
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2025 |
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20th International Symposium on Visual Computing (ISVC 2025) |
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Call Number |
cidis @ cidis @ |
Serial |
292 |
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Author |
Benítez-Quintero J., Quevedo-Pinos O., Calderon, Fernanda |
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Title |
Notes on Sulfur Fluxes in Urban Areas with Industrial Activity |
Type |
Conference Article |
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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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Call Number |
cidis @ cidis @ |
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201 |
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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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Year |
2018 |
Publication |
25 th IEEE International Conference on Image Processing, ICIP 2018 |
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2237-2241 |
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Abstract |
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 |
Mehri, A, Ardakani, P.B., Sappa, A.D. |
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Title |
LiNet: A Lightweight Network for Image Super Resolution |
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Conference Article |
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2021 |
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25th International Conference on Pattern Recognition (ICPR), enero 10-15, 2021 |
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7196-7202 |
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cidis @ cidis @ |
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149 |
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