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Author Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla
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 Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla
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 Milton Mendieta; F. Panchana; B. Andrade; B. Bayot; C. Vaca; Boris X. Vintimilla; Dennis G. Romero
Title Organ identification on shrimp histological images: A comparative study considering CNN and feature engineering. Type Conference Article
Year 2018 Publication IEEE Ecuador Technical Chapters Meeting ETCM 2018. Cuenca, Ecuador Abbreviated Journal
Volume Issue Pages 1-6
Keywords
Abstract The identification of shrimp organs in biology using

histological images is a complex task. Shrimp histological images

poses a big challenge due to their texture and similarity among

classes. Image classification by using feature engineering and

convolutional neural networks (CNN) are suitable methods to

assist biologists when performing organ detection. This work

evaluates the Bag-of-Visual-Words (BOVW) and Pyramid-Bagof-

Words (PBOW) models for image classification leveraging big

data techniques; and transfer learning for the same classification

task by using a pre-trained CNN. A comparative analysis

of these two different techniques is performed, highlighting

the characteristics of both approaches on the shrimp organs

identification problem.
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Call Number gtsi @ user @ Serial 87
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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 Abbreviated Journal
Volume Issue Pages
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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 Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla
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
Keywords
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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Call Number gtsi @ user @ Serial 82
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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 Abbreviated Journal
Volume Issue Pages 2237-2241
Keywords
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 Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla
Title Adaptive Harris Corners Detector Evaluated with Cross-Spectral Images Type Conference Article
Year 2018 Publication International Conference on Information Technology & Systems (ICITS 2018). ICITS 2018. Advances in Intelligent Systems and Computing Abbreviated Journal
Volume 721 Issue Pages
Keywords
Abstract This paper proposes a novel approach to use cross-spectral

images to achieve a better performance with the proposed Adaptive Harris

corner detector comparing its obtained results with those achieved

with images of the visible spectra. The images of urban, field, old-building

and country category were used for the experiments, given the variety of

the textures present in these images, with which the complexity of the

proposal is much more challenging for its verification. It is a new scope,

which means improving the detection of characteristic points using crossspectral

images (NIR, G, B) and applying pruning techniques, the combination

of channels for this fusion is the one that generates the largest

variance based on the intensity of the merged pixels, therefore, it is that

which maximizes the entropy in the resulting Cross-spectral images.

Harris is one of the most widely used corner detection algorithm, so

any improvement in its efficiency is an important contribution in the

field of computer vision. The experiments conclude that the inclusion of

a (NIR) channel in the image as a result of the combination of the spectra,

greatly improves the corner detection due to better entropy of the

resulting image after the fusion, Therefore the fusion process applied to

the images improves the results obtained in subsequent processes such as

identification of objects or patterns, classification and/or segmentation.
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Notes 1 Approved no
Call Number gtsi @ user @ Serial 84
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Author Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla
Title Cross-spectral image dehaze through a dense stacked conditional GAN based approach. Type Conference Article
Year 2018 Publication 14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018) Abbreviated Journal
Volume Issue Pages 358-364
Keywords
Abstract This paper proposes a novel approach to remove haze from RGB images using a near infrared images based on a dense stacked conditional Generative Adversarial Network (CGAN). The architecture of the deep network implemented receives, besides the images with haze, its corresponding image in the near infrared spectrum, which serve to accelerate the learning process of the details of the characteristics of the images. The model uses a triplet layer that allows the independence learning of each channel of the visible spectrum image to remove the haze on each color channel separately. A multiple loss function scheme is proposed, which ensures balanced learning between the colors and the structure of the images. Experimental results have shown that the proposed method effectively removes the haze from the images. Additionally, the proposed approach is compared with a state of the art approach showing better results.
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Notes Approved no
Call Number gtsi @ user @ Serial 92
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Author Dennis G. Romero; A. F. Neto; T. F. Bastos; Boris X. Vintimilla
Title RWE patterns extraction for on-line human action recognition through window-based analysis of invariant moments Type Conference Article
Year 2012 Publication 5th Workshop in applied Robotics and Automation (RoboControl) Abbreviated Journal
Volume Issue Pages
Keywords Human action recognition, Relative Wavelet Energy, Window-based temporal analysis.
Abstract This paper presents a method for on-line human action recognition on video sequences. An analysis based on Mahalanobis distance is performed to identify the “idle” state, which defines the beginning and end of the person movement, for posterior patterns extraction based on Relative Wavelet Energy from sequences of invariant moments.
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Language English Summary Language English Original Title
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Notes Approved no
Call Number cidis @ cidis @ Serial 23
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Author Dennis G. Romero; A. F. Neto; T. F. Bastos; Boris X. Vintimilla
Title An approach to automatic assistance in physiotherapy based on on-line movement identification. Type Conference Article
Year 2012 Publication VI Andean Region International Conference – ANDESCON 2012 Abbreviated Journal
Volume Issue Pages
Keywords patient rehabilitation, patient treatment, statistical analysis
Abstract This paper describes a method for on-line movement identification, oriented to patient’s movement evaluation during physiotherapy. An analysis based on Mahalanobis distance between temporal windows is performed to identify the “idle/motion” state, which defines the beginning and end of the patient’s movement, for posterior patterns extraction based on Relative Wavelet Energy from sequences of invariant moments.
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Publisher IEEE Place of Publication Andean Region International Conference (ANDESCON), 2012 VI Editor
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Notes Approved no
Call Number cidis @ cidis @ Serial 24
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