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Author Mehri, A, Ardakani, P.B., Sappa, A.D. pdf  openurl
  Title (down) 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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  Call Number cidis @ cidis @ Serial 149  
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Author Cristhian A. Aguilera; Francisco J. Aguilera; Angel D. Sappa; Ricardo Toledo pdf  openurl
  Title (down) Learning crossspectral similarity measures with deep convolutional neural networks Type Conference Article
  Year 2016 Publication IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) Workshops Abbreviated Journal  
  Volume Issue Pages 267-275  
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  Abstract The simultaneous use of images from different spectra can be helpful to improve the performance of many com- puter vision tasks. The core idea behind the usage of cross- spectral approaches is to take advantage of the strengths of each spectral band providing a richer representation of a scene, which cannot be obtained with just images from one spectral band. In this work we tackle the cross-spectral image similarity problem by using Convolutional Neural Networks (CNNs). We explore three different CNN archi- tectures to compare the similarity of cross-spectral image patches. Specifically, we train each network with images from the visible and the near-infrared spectrum, and then test the result with two public cross-spectral datasets. Ex- perimental results show that CNN approaches outperform the current state-of-art on both cross-spectral datasets. Ad- ditionally, our experiments show that some CNN architec- tures are capable of generalizing between different cross- spectral domains.  
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  Call Number cidis @ cidis @ Serial 48  
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Author Xavier Soria , Gonzalo Pomboza-Junez & Angel Sappa. url  openurl
  Title (down) LDC: Lightweight Dense CNN for Edge Detection. Type Journal Article
  Year 2022 Publication IEEE Access journal Abbreviated Journal  
  Volume Vol. 10 Issue Pages pp. 68281-68290  
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  Call Number cidis @ cidis @ Serial 183  
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Author Morocho-Cayamcela, M.E. & W. Lim pdf  openurl
  Title (down) 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  
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  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.		  
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  Call Number cidis @ cidis @ Serial 139  
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Author Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla pdf  openurl
  Title (down) 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 62  
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Author Miguel Oliveira; Vítor Santos; Angel D. Sappa; Paulo Dias; A. Paulo Moreira pdf  url
openurl 
  Title (down) Incremental Texture Mapping for Autonomous Driving Type Journal Article
  Year 2016 Publication Robotics and Autonomous Systems Journal Abbreviated Journal  
  Volume Vol. 84 Issue Pages pp. 113-128  
  Keywords Scene reconstruction, Autonomous driving, Texture mapping  
  Abstract Autonomous vehicles have a large number of on-board sensors, not only for providing coverage all around the vehicle, but also to ensure multi-modality in the observation of the scene. Because of this, it is not trivial to come up with a single, unique representation that feeds from the data given by all these sensors. We propose an algorithm which is capable of mapping texture collected from vision based sensors onto a geometric description of the scenario constructed from data provided by 3D sensors. The algorithm uses a constrained Delaunay triangulation to produce a mesh which is updated using a specially devised sequence of operations. These enforce a partial configuration of the mesh that avoids bad quality textures and ensures that there are no gaps in the texture. Results show that this algorithm is capable of producing fine quality textures.  
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  Call Number cidis @ cidis @ Serial 50  
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Author Miguel Oliveira; Vítor Santos; Angel D. Sappa; Paulo Dias; A. Paulo Moreira pdf  url
openurl 
  Title (down) Incremental Scenario Representations for Autonomous Driving using Geometric Polygonal Primitives Type Journal Article
  Year 2016 Publication Robotics and Autonomous Systems Journal Abbreviated Journal  
  Volume Vol. 83 Issue Pages pp. 312-325  
  Keywords Incremental scene reconstructionPoint cloudsAutonomous vehiclesPolygonal primitives  
  Abstract When an autonomous vehicle is traveling through some scenario it receives a continuous stream of sensor data. This sensor data arrives in an asynchronous fashion and often contains overlapping or redundant information. Thus, it is not trivial how a representation of the environment observed by the vehicle can be created and updated over time. This paper presents a novel methodology to compute an incremental 3D representation of a scenario from 3D range measurements. We propose to use macro scale polygonal primitives to model the scenario. This means that the representation of the scene is given as a list of large scale polygons that describe the geometric structure of the environment. Furthermore, we propose mechanisms designed to update the geometric polygonal primitives over time whenever fresh sensor data is collected. Results show that the approach is capable of producing accurate descriptions of the scene, and that it is computationally very efficient when compared to other reconstruction techniques.  
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  Call Number cidis @ cidis @ Serial 49  
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Author Morocho-Cayamcela, M.E. pdf  openurl
  Title (down) 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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  Call Number cidis @ cidis @ Serial 140  
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Author Xavier Soria; Angel D. Sappa openurl 
  Title (down) 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 Velez R., Paredes A., Silva S., Paillacho D., and Paillacho J. url  openurl
  Title (down) Implementation of a UVC lights disinfection system for a diferential robot applying security methods in indoor. Type Conference Article
  Year 2022 Publication Communications in Computer and Information Science, International Conference on Applied Technologies (ICAT 2021), octubre 27-29 Abbreviated Journal CONFERENCE  
  Volume 1535 Issue Pages 319-331  
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  Call Number cidis @ cidis @ Serial 178  
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