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Author |
Patricia Suarez, Henry Velesaca, Dario Carpio, Angel Sappa, Patricia Urdiales, Francisca Burgos |
Title |
Deep Learning based Shrimp Classification |
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Conference Article |
Year |
2022 |
Publication |
17th International Symposium on Visual Computing, San Diego, USA, Octubre 3-5. Lecture Notes in Computer Science (LNCS) |
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13598 LNCS |
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36-45 |
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no |
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cidis @ cidis @ |
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194 |
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Author |
Patricia Súarez, Henry Velesaca, Dario Carpio & Angel Sappa |
Title |
Corn Kernel Classification From Few Training Samples |
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Journal Article |
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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no |
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cidis @ cidis @ |
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223 |
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Author |
Patricia Suarez, Angel Sappa |
Title |
Depth-Conditioned Thermal-like Image Generation |
Type |
Conference Article |
Year |
2024 |
Publication |
14th International Conference on Pattern Recognition Systems (ICPRS) Londres 15 – 18 July 2024 |
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no |
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cidis @ cidis @ |
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243 |
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Author |
Patricia Suarez, Angel D. Sappa |
Title |
A Generative Model for Guided Thermal Image Super-Resolution |
Type |
Conference Article |
Year |
2024 |
Publication |
Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2024) Rome 27 – 29 February 2024 |
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Vol. 3: VISAPP |
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765-771 |
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no |
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cidis @ cidis @ |
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240 |
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Author |
Patricia Suarez Riofrio & Angel D. Sappa |
Title |
Thermal Image Synthesis: Bridging the Gap between Visible and Infrared Spectrum |
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Conference Article |
Year |
2024 |
Publication |
Accepted in 19th International Symposium on Visual Computing 2024 |
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no |
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cidis @ cidis @ |
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253 |
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Author |
Patricia Suarez & Angel Sappa |
Title |
Toward a thermal image-like representation |
Type |
Conference Article |
Year |
2023 |
Publication |
Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) Lisbon, 19-21 Febrero 2023 |
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133 - 140 |
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no |
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cidis @ cidis @ |
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205 |
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Author |
Patricia Suarez & Angel D. Sappa |
Title |
Haze-Free Imaging through Haze-Aware Transformer Adaptations |
Type |
Conference Article |
Year |
2024 |
Publication |
In Fourth International Conference on Innovations in Computational Intelligence and Computer Vision (ICICV 2024) |
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no |
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cidis @ cidis @ |
Serial |
236 |
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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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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 |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla; Riad I. Hammoud |
Title |
Image Vegetation Index through a Cycle Generative Adversarial Network |
Type |
Conference Article |
Year |
2019 |
Publication |
Conference on Computer Vision and Pattern Recognition Workshops (CVPR 2019); Long Beach, California, United States |
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1014-1021 |
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Abstract |
This paper proposes a novel approach to estimate the
Normalized Difference Vegetation Index (NDVI) just from
an RGB image. The NDVI values are obtained by using
images from the visible spectral band together with a synthetic near infrared image obtained by a cycled GAN. The
cycled GAN network is able to obtain a NIR image from
a given gray scale image. It is trained by using unpaired
set of gray scale and NIR images by using a U-net architecture and a multiple loss function (gray scale images are
obtained from the provided RGB images). Then, the NIR
image estimated with the proposed cycle generative adversarial network is used to compute the NDVI index. Experimental results are provided showing the validity of the proposed approach. Additionally, comparisons with previous
approaches are also provided. |
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no |
Call Number |
gtsi @ user @ |
Serial |
106 |
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Author |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla; Riad I. Hammoud |
Title |
Deep Learning based Single Image Dehazing |
Type |
Conference Article |
Year |
2018 |
Publication |
14th IEEE Workshop on Perception Beyond the Visible Spectrum – In conjunction with CVPR 2018. Salt Lake City, Utah. USA |
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This paper proposes a novel approach to remove haze
degradations in RGB images using a stacked conditional
Generative Adversarial Network (GAN). It employs a triplet
of GAN to remove the haze on each color channel independently.
A multiple loss functions scheme, applied over a
conditional probabilistic model, is proposed. The proposed
GAN architecture learns to remove the haze, using as conditioned
entrance, the images with haze from which the clear
images will be obtained. Such formulation ensures a fast
model training convergence and a homogeneous model generalization.
Experiments showed that the proposed method
generates high-quality clear images. |
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gtsi @ user @ |
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83 |
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