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Author Jacome-Galarza L.-R pdf  openurl
  Title Crop yield prediction utilizing multimodal deep learning Type Conference Article
  Year 2021 Publication 16th Iberian Conference on Information Systems and Technologies, CISTI 2021, junio 23 – 26, 2021 Abbreviated Journal  
  Volume Issue Pages  
  Keywords Agricultura de precisión; sensores remotos; aprendizaje profundo multimodal; IoT; agentes inteligentes; computación aplicada.  
  Abstract La agricultura de precisión es una práctica vital para

mejorar la producción de cosechas. El presente trabajo tiene

como objetivo desarrollar un modelo multimodal de aprendizaje

profundo que es capaz de producir un mapa de salud de

cosechas. El modelo recibe como entradas imágenes multiespectrales

y datos de sensores de campo (humedad,

temperatura, estado del suelo, etc.) y crea un mapa de

rendimiento de la cosecha. La utilización de datos multimodales

tiene como finalidad extraer patrones ocultos del estado de salud

de las cosechas y de esta manera obtener mejores resultados que

los obtenidos mediante los índices de vegetación.		  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Español Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial (down) 150  
Permanent link to this record
 
 
Author Mehri, A, Ardakani, P.B., Sappa, A.D. pdf  openurl
  Title LiNet: A Lightweight Network for Image Super Resolution Type Conference Article
  Year 2021 Publication 25th International Conference on Pattern Recognition (ICPR), enero 10-15, 2021 Abbreviated Journal  
  Volume Issue Pages 7196-7202  
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  Area Expedition Conference  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial (down) 149  
Permanent link to this record
 
 
Author Mehri, A, Ardakani, P.B., Sappa, A.D. pdf  openurl
  Title MPRNet: Multi-Path Residual Network for Lightweight Image Super Resolution. Type Conference Article
  Year 2021 Publication In IEEE Winter Conference on Applications of Computer Vision WACV 2021, enero 5-9, 2021 Abbreviated Journal  
  Volume Issue Pages 2703-2712  
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  Series Editor Series Title Abbreviated Series Title  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial (down) 148  
Permanent link to this record
 
 
Author Charco, J.L., Sappa, A.D., Vintimilla, B.X., Velesaca, H.O. pdf  openurl
  Title Camera pose estimation in multi-view environments:from virtual scenarios to the real world Type Journal Article
  Year 2021 Publication In Image and Vision Computing Journal. (Article number 104182) Abbreviated Journal  
  Volume Vol. 110 Issue Pages  
  Keywords Relative camera pose estimation, Domain adaptation, Siamese architecture, Synthetic data, Multi-view environments  
  Abstract This paper presents a domain adaptation strategy to efficiently train network architectures for estimating the relative camera pose in multi-view scenarios. The network architectures are fed by a pair of simultaneously acquired

images, hence in order to improve the accuracy of the solutions, and due to the lack of large datasets with pairs of

overlapped images, a domain adaptation strategy is proposed. The domain adaptation strategy consists on transferring the knowledge learned from synthetic images to real-world scenarios. For this, the networks are firstly

trained using pairs of synthetic images, which are captured at the same time by a pair of cameras in a virtual environment; and then, the learned weights of the networks are transferred to the real-world case, where the networks are retrained with a few real images. Different virtual 3D scenarios are generated to evaluate the

relationship between the accuracy on the result and the similarity between virtual and real scenarios—similarity

on both geometry of the objects contained in the scene as well as relative pose between camera and objects in the

scene. Experimental results and comparisons are provided showing that the accuracy of all the evaluated networks for estimating the camera pose improves when the proposed domain adaptation strategy is used,

highlighting the importance on the similarity between virtual-real scenarios.		  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial (down) 147  
Permanent link to this record
 
 
Author Rosero Vasquez Shendry url  openurl
  Title Facial recognition: traditional methods vs. methods based on deep learning. Advances in Intelligent Systems and Computing – Information Technology and Systems Proceedings of ICITS 2020. Type Journal Article
  Year 2020 Publication Abbreviated Journal  
  Volume Issue Pages 615-625  
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  Area Expedition Conference  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial (down) 145  
Permanent link to this record
 
 
Author Patricia L. Suarez pdf  openurl
  Title Procesamiento y representación de imágenes multiespectrales usando técnicas de aprendizaje profundo (Ph.D. Angel Sappa, Director & Ph.D. Boris Vintimilla, Codirector.). Ph.D. thesis. Type Book Chapter
  Year 2020 Publication Ediciones FIEC-ESPOL. Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract  
  Address  
  Corporate Author Ph.D. Angel Sappa, Director & Ph.D. Boris Vintimilla, Codirector. Thesis  
  Publisher Place of Publication Editor  
  Language Español Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial (down) 144  
Permanent link to this record
 
 
Author Morocho-Cayamcela, M.E. pdf  openurl
  Title Increasing the Segmentation Accuracy of Aerial Images with Dilated Spatial Pyramid Pooling Type Journal Article
  Year 2020 Publication Electronic Letters on Computer Vision and Image Analysis (ELCVIA) Abbreviated Journal  
  Volume Vol. 19 Issue Issue 2 Pages pp. 17-21  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial (down) 140  
Permanent link to this record
 
 
Author Morocho-Cayamcela, M.E. & W. Lim pdf  openurl
  Title Lateral confinement of high-impedance surface-waves through reinforcement learning Type Journal Article
  Year 2020 Publication Electronics Letters Abbreviated Journal  
  Volume Vol. 56 Issue 23, 12 November 2020 Pages pp. 1262-1264  
  Keywords  
  Abstract The authors present a model-free policy-based reinforcement learning

model that introduces perturbations on the pattern of a metasurface.

The objective is to learn a policy that changes the size of the

patches, and therefore the impedance in the sides of an artificially structured

material. The proposed iterative model assigns the highest reward

when the patch sizes allow the transmission along a constrained path

and penalties when the patch sizes make the surface wave radiate to

the sides of the metamaterial. After convergence, the proposed

model learns an optimal patch pattern that achieves lateral confinement

along the metasurface. Simulation results show that the proposed

learned-pattern can effectively guide the electromagnetic wave

through a metasurface, maintaining its instantaneous eigenstate when

the homogeneity is perturbed. Moreover, the pattern learned to

prevent reflections by changing the patch sizes adiabatically. The

reflection coefficient S1, 2 shows that most of the power gets transferred

from the source to the destination with the proposed design.		  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial (down) 139  
Permanent link to this record
 
 
Author Patricia L. Suárez, Angel D. Sappa and Boris X. Vintimilla url  openurl
  Title Deep learning-based vegetation index estimation Type Book Chapter
  Year 2021 Publication Generative Adversarial Networks for Image-to-Image Translation Book. Abbreviated Journal  
  Volume Chapter 9 Issue Issue 2 Pages 205-232  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial (down) 137  
Permanent link to this record
 
 
Author Ángel Morera, Ángel Sánchez, A. Belén Moreno, Angel D. Sappa, & José F. Vélez pdf  isbn
openurl 
  Title SSD vs. YOLO for Detection of Outdoor Urban Advertising Panels under Multiple Variabilities. Type Journal Article
  Year 2020 Publication Abbreviated Journal In Sensors  
  Volume Vol. 2020-August Issue 16 Pages pp. 1-23  
  Keywords object detection; urban outdoor panels; one-stage detectors; Single Shot MultiBox Detector (SSD); You Only Look Once (YOLO); detection metrics; object and scene imaging variabilities  
  Abstract This work compares Single Shot MultiBox Detector (SSD) and You Only Look Once (YOLO)

deep neural networks for the outdoor advertisement panel detection problem by handling multiple

and combined variabilities in the scenes. Publicity panel detection in images o ers important

advantages both in the real world as well as in the virtual one. For example, applications like Google

Street View can be used for Internet publicity and when detecting these ads panels in images, it could

be possible to replace the publicity appearing inside the panels by another from a funding company.

In our experiments, both SSD and YOLO detectors have produced acceptable results under variable

sizes of panels, illumination conditions, viewing perspectives, partial occlusion of panels, complex

background and multiple panels in scenes. Due to the diculty of finding annotated images for the

considered problem, we created our own dataset for conducting the experiments. The major strength

of the SSD model was the almost elimination of False Positive (FP) cases, situation that is preferable

when the publicity contained inside the panel is analyzed after detecting them. On the other side,

YOLO produced better panel localization results detecting a higher number of True Positive (TP)

panels with a higher accuracy. Finally, a comparison of the two analyzed object detection models

with di erent types of semantic segmentation networks and using the same evaluation metrics is

also included.		  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN 14248220 Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial (down) 133  
Permanent link to this record
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