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Author (down) Patricia Suarez, Henry Velesaca, Dario Carpio, Angel Sappa, Patricia Urdiales, Francisca Burgos url  openurl
  Title Deep Learning based Shrimp Classification Type Conference Article
  Year 2022 Publication 17th International Symposium on Visual Computing, San Diego, USA, Octubre 3-5. Lecture Notes in Computer Science (LNCS) Abbreviated Journal  
  Volume 13598 LNCS Issue Pages 36-45  
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  Call Number cidis @ cidis @ Serial 194  
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Author (down) Patricia Súarez, Henry Velesaca, Dario Carpio & Angel Sappa url  openurl
  Title Corn Kernel Classification From Few Training Samples Type Journal Article
  Year 2023 Publication In journal Artificial Intelligence in Agriculture Abbreviated Journal  
  Volume Vol. 9 Issue Pages pp. 89-99  
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  Call Number cidis @ cidis @ Serial 223  
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Author (down) Patricia Suarez, Dario Carpio & Angel Sappa openurl 
  Title A Deep Learning based Approach to Synthesized Realistic Depth Images Type Conference Article
  Year 2023 Publication accepted in 22nd International Conference on Image Analysis and Processing ICIAP 2023 Abbreviated Journal  
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  Call Number cidis @ cidis @ Serial 218  
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Author (down) Patricia Suarez & Angel Sappa pdf  openurl
  Title Toward a thermal image-like representation Type Conference Article
  Year 2023 Publication 18th International Conference on Computer Vision Theory and Applications VISAPP 2023 Abbreviated Journal  
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  Call Number cidis @ cidis @ Serial 205  
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Author (down) Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla; Riad I. Hammoud pdf  openurl
  Title Near InfraRed Imagery Colorization Type Conference Article
  Year 2018 Publication 25 th IEEE International Conference on Image Processing, ICIP 2018 Abbreviated Journal  
  Volume Issue Pages 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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  Call Number gtsi @ user @ Serial 81  
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Author (down) Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla; Riad I. Hammoud pdf  openurl
  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 Abbreviated Journal  
  Volume Issue Pages 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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  Call Number gtsi @ user @ Serial 106  
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Author (down) Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla; Riad I. Hammoud pdf  openurl
  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 Abbreviated Journal  
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  Abstract 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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  Call Number gtsi @ user @ Serial 83  
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Author (down) Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla pdf  openurl
  Title Colorizing Infrared Images through a Triplet Condictional DCGAN Architecture Type Conference Article
  Year 2017 Publication 19th International Conference on Image Analysis and Processing. Abbreviated Journal  
  Volume Issue Pages 287-297  
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  Call Number gtsi @ user @ Serial 66  
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Author (down) Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla pdf  openurl
  Title Learning Image Vegetation Index through a Conditional Generative Adversarial Network Type Conference Article
  Year 2017 Publication 2nd IEEE Ecuador Tehcnnical Chapters Meeting (ETCM) Abbreviated Journal  
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  Call Number gtsi @ user @ Serial 70  
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Author (down) Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla pdf  url
openurl 
  Title Vegetation Index Estimation from Monospectral Images Type Conference Article
  Year 2018 Publication 15th International Conference, Image Analysis and Recognition (ICIAR 2018), Póvoa de Varzim, Portugal. Lecture Notes in Computer Science Abbreviated Journal  
  Volume 10882 Issue Pages 353-362  
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  Abstract This paper proposes a novel approach to estimate Normalized

Difference Vegetation Index (NDVI) from just the red channel of

a RGB image. The NDVI index is defined as the ratio of the difference

of the red and infrared radiances over their sum. In other words, information

from the red channel of a RGB image and the corresponding

infrared spectral band are required for its computation. In the current

work the NDVI index is estimated just from the red channel by training a

Conditional Generative Adversarial Network (CGAN). The architecture

proposed for the generative network consists of a single level structure,

which combines at the final layer results from convolutional operations

together with the given red channel with Gaussian noise to enhance

details, resulting in a sharp NDVI image. Then, the discriminative model

estimates the probability that the NDVI generated index came from the

training dataset, rather than the index automatically generated. Experimental

results with a large set of real images are provided showing that

a Conditional GAN single level model represents an acceptable approach

to estimate NDVI index.		  
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  Notes Approved no  
  Call Number gtsi @ user @ Serial 82  
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