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Author Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla pdf  url
openurl 
  Title (up) 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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  Call Number gtsi @ user @ Serial 84  
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Author Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla pdf  openurl
  Title (up) 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 Jorge L. Charco; Boris X. Vintimilla; Angel D. Sappa pdf  openurl
  Title (up) Deep learning based camera pose estimation in multi-view environment. Type Conference Article
  Year 2018 Publication 14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018) Abbreviated Journal  
  Volume Issue Pages 224-228  
  Keywords  
  Abstract This paper proposes to use a deep learning network architecture for relative camera pose estimation on a multi-view environment. The proposed network is a variant architecture of AlexNet to use as regressor for prediction the relative translation and rotation as output. The proposed approach is trained from scratch on a large data set that takes as input a pair of images from the same scene. This new architecture is compared with a previous approach using standard metrics, obtaining better results on the relative camera pose.  
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  Notes Approved no  
  Call Number gtsi @ user @ Serial 93  
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Author Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla; Riad I. Hammoud pdf  openurl
  Title (up) 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  
  Keywords  
  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 Alex Ferrin; Julio Larrea; Miguel Realpe; Daniel Ochoa pdf  openurl
  Title (up) Detection of utility poles from noisy Point Cloud Data in Urban environments. Type Conference Article
  Year 2018 Publication Artificial Intelligence and Cloud Computing Conference (AICCC 2018) Abbreviated Journal  
  Volume Issue Pages 53-57  
  Keywords  
  Abstract In recent years 3D urban maps have become more common, thus providing complex point clouds that include diverse urban furniture such as pole-like objects. Utility poles detection in urban environment is of particular interest for electric utility companies in order to maintain an updated inventory for better planning and management. The present study develops an automatic method for the detection of utility poles from noisy point cloud data of Guayaquil – Ecuador, where many poles are located next to buildings, or houses are built until the border of the sidewalk getting very close to poles, which increases the difficulty of discriminating poles, walls, columns, fences and building corners.  
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  Call Number gtsi @ user @ Serial 94  
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Author Gomer Rubio; Wilton Agila openurl 
  Title (up) Dynamic Modeling of Fuel Cells in a Strategic Context Type Conference Article
  Year 2018 Publication 7th International Conference on Renewable Energy Research and Applications, ICRERA 2018. Paris, Francia. Abbreviated Journal  
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  Call Number gtsi @ user @ Serial 86  
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Author Xavier Soria; Angel D. Sappa openurl 
  Title (up) Improving Edge Detection in RGB Images by Adding NIR Channel. Type Conference Article
  Year 2018 Publication 14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018) Abbreviated Journal  
  Volume Issue Pages 266-273  
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  Call Number gtsi @ user @ Serial 95  
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Author Cristhian A. Aguilera; Cristhian Aguilera; Angel D. Sappa pdf  openurl
  Title (up) Melamine faced panels defect classification beyond the visible spectrum. Type Journal Article
  Year 2018 Publication In Sensors 2018 Abbreviated Journal  
  Volume Vol. 11 Issue Issue 11 Pages  
  Keywords  
  Abstract In this work, we explore the use of images from different spectral bands to classify defects in melamine faced panels, which could appear through the production process. Through experimental evaluation, we evaluate the use of images from the visible (VS), near-infrared (NIR), and long wavelength infrared (LWIR), to classify the defects using a feature descriptor learning approach together with a support vector machine classifier. Two descriptors were evaluated, Extended Local Binary Patterns (E-LBP) and SURF using a Bag of Words (BoW) representation. The evaluation was carried on with an image set obtained during this work, which contained five different defect categories that currently occurs in the industry. Results show that using images from beyond

the visual spectrum helps to improve classification performance in contrast with a single visible spectrum solution.
 
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  Notes Approved no  
  Call Number gtsi @ user @ Serial 89  
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Author Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla; Riad I. Hammoud pdf  openurl
  Title (up) 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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  Notes Approved no  
  Call Number gtsi @ user @ Serial 81  
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Author Milton Mendieta; F. Panchana; B. Andrade; B. Bayot; C. Vaca; Boris X. Vintimilla; Dennis G. Romero pdf  openurl
  Title (up) 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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  Area Expedition Conference  
  Notes Approved no  
  Call Number gtsi @ user @ Serial 87  
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