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Author 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  
  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 (down) 82  
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Author 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  
  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 (down) 81  
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Author 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 (down) 70  
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Author 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 (down) 66  
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Author Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla pdf  openurl
  Title Infrared Image Colorization based on a Triplet DCGAN Architecture. Type Conference Article
  Year 2017 Publication 13th IEEE Workshop on Perception Beyond the Visible Spectrum – In conjunction with CVPR 2017. (This paper has been selected as “Best Paper Award” ) Abbreviated Journal  
  Volume 2017-July Issue Pages 212-217  
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  Call Number cidis @ cidis @ Serial (down) 62  
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Author Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla pdf  openurl
  Title Learning to Colorize Infrared Images Type Conference Article
  Year 2017 Publication 15th International Conference on Practical Applications of Agents and Multi-Agent Systems Abbreviated Journal  
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  Call Number cidis @ cidis @ Serial (down) 58  
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Author Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla pdf  openurl
  Title Cross-spectral Image Patch Similarity using Convolutional Neural Network Type Conference Article
  Year 2017 Publication 2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics (ECMSM) Abbreviated Journal  
  Volume Issue Pages 1-5  
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  Call Number cidis @ cidis @ Serial (down) 57  
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Author Sianna Puente; Cindy Madrid; Miguel Realpe; Boris X. Vintimilla pdf  openurl
  Title An Empirical Comparison of DCNN libraries to implement the Vision Module of a Danger Management System Type Conference Article
  Year 2017 Publication 2017 International Conference on Deep Learning Technologies (ICDLT 2017) Abbreviated Journal  
  Volume Part F128535 Issue Pages 60-65  
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  Call Number cidis @ cidis @ Serial (down) 56  
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Author Angely Oyola; Dennis G. Romero; Boris X. Vintimilla pdf  openurl
  Title A Dijkstra-based algorithm for selecting the Shortest-Safe Evacuation Routes in dynamic environments (SSER) Type Conference Article
  Year 2017 Publication The 30th International Conference on Industrial, Engineering, Other Applications of Applied Intelligent Systems (IEA/AIE 2017) Abbreviated Journal  
  Volume Issue Pages 131-135  
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  Call Number cidis @ cidis @ Serial (down) 55  
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Author Angel D. Sappa; Cristhian A. Aguilera; Juan A. Carvajal Ayala; Miguel Oliveira; Dennis Romero; Boris X. Vintimilla; Ricardo Toledo pdf  url
doi  openurl
  Title Monocular visual odometry: a cross-spectral image fusion based approach Type Journal Article
  Year 2016 Publication Robotics and Autonomous Systems Journal Abbreviated Journal  
  Volume Vol. 86 Issue Pages pp. 26-36  
  Keywords Monocular visual odometry LWIR-RGB cross-spectral imaging Image fusion  
  Abstract This manuscript evaluates the usage of fused cross-spectral images in a monocular visual odometry approach. Fused images are obtained through a Discrete Wavelet Transform (DWT) scheme, where the best setup is em- pirically obtained by means of a mutual information based evaluation met- ric. The objective is to have a exible scheme where fusion parameters are adapted according to the characteristics of the given images. Visual odom- etry is computed from the fused monocular images using an off the shelf approach. Experimental results using data sets obtained with two different platforms are presented. Additionally, comparison with a previous approach as well as with monocular-visible/infrared spectra are also provided showing the advantages of the proposed scheme.  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial (down) 54  
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