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Author Dennis G. Romero; Anselmo Frizera N.; Teodiano Freire B. pdf  url
openurl 
  Title Reconocimiento en-l?nea de acciones humanas basado en patrones de RWE aplicado en ventanas dinámicas de momentos invariantes Type Journal Article
  Year 2014 Publication Revista Iberoamericana de Automática e Informática industrial 00 (2014) Abbreviated Journal  
  Volume 11 Issue Pages 202-211  
  Keywords Vision por ordenador, Mapas de profundidad, Reconocimiento de acciones humanas, Relative Wavelet Energy, Distancia de ´ Mahalanobis  
  Abstract Durante los últimos años ha existido un fuerte incremento en el acceso a internet, causando que los centros de datos (DC) deban adaptar dinámicamente su infraestructura de red de cara a enfrentar posibles problemas de congestión, la cual no siempre se da de forma oportuna. Ante esto, nuevas topologías de red se han propuesto en los últimos años, como una forma de brindar mejores condiciones para el manejo de tráfico interno, sin embargo es común que para el estudio de estas mejoras, se necesite recrear el comportamiento de un verdadero DC en modelos de simulación/emulación. Por lo tanto se vuelve esencial validar dichos modelos, de cara a obtener resultados coherentes con la realidad. Esta validación es posible por medio de la identificación de ciertas propiedades que se deducen a partir de las variables y los parámetros que describen la red, y que se mantienen en las topologías de los DC para diversos escenarios y/o configuraciones. Estas propiedades, conocidas como invariantes, son una expresión del funcionamiento de la red en ambientes reales, como por ejemplo la ruta más larga entre dos nodos o el número de enlaces mínimo que deben fallar antes de una pérdida de conectividad en alguno de los nodos de la red. En el presente trabajo se realiza la identificación, formulación y comprobación de dos invariantes para la topología Fat-Tree, utilizando como software emulador a mininet. Las conclusiones muestran resultados concordantes entre lo analítico y lo práctico.  
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  Language Español Summary Language Español Original Title  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 30  
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Author Angel D. Sappa; Juan A. Carvajal; Cristhian A. Aguilera; Miguel Oliveira; Dennis G. Romero; Boris X. Vintimilla pdf  url
openurl 
  Title Wavelet-Based Visible and Infrared Image Fusion: A Comparative Study Type Journal Article
  Year 2016 Publication Sensors Journal Abbreviated Journal  
  Volume vol 16 Issue Pages 1-15  
  Keywords image fusion; fusion evaluation metrics; visible and infrared imaging; discrete wavelet transform  
  Abstract This paper evaluates different wavelet-based cross-spectral image fusion strategies adopted to merge visible and infrared images. The objective is to find the best setup independently of the evaluation metric used to measure the performance. Quantitative performance results are obtained with state of the art approaches together with adaptations proposed in the current work. The options evaluated in the current work result from the combination of different setups in the wavelet image decomposition stage together with different fusion strategies for the final merging stage that generates the resulting representation. Most of the approaches evaluate results according to the application for which they are intended for. Sometimes a human observer is selected to judge the quality of the obtained results. In the current work, quantitative values are considered in order to find correlations between setups and performance of obtained results; these correlations can be used to define a criteria for selecting the best fusion strategy for a given pair of cross-spectral images. The whole procedure is evaluated with a large set of correctly registered visible and infrared image pairs, including both Near InfraRed (NIR) and LongWave InfraRed (LWIR).  
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  Language English Summary Language English Original Title  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 47  
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Author Miguel Oliveira; Vítor Santos; Angel D. Sappa; Paulo Dias; A. Paulo Moreira pdf  url
openurl 
  Title Incremental Scenario Representations for Autonomous Driving using Geometric Polygonal Primitives Type Journal Article
  Year 2016 Publication Robotics and Autonomous Systems Journal Abbreviated Journal  
  Volume 83 Issue Pages 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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  Language English Summary Language English Original Title  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 49  
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Author Miguel Oliveira; Vítor Santos; Angel D. Sappa; Paulo Dias; A. Paulo Moreira pdf  url
openurl 
  Title Incremental Texture Mapping for Autonomous Driving Type Journal Article
  Year 2016 Publication Robotics and Autonomous Systems Journal Abbreviated Journal  
  Volume 84 Issue Pages 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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  Language English Summary Language English Original Title  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 50  
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Author Miguel Realpe; Boris X. Vintimilla; Ljubo Vlacic pdf  openurl
  Title Multi-sensor Fusion Module in a Fault Tolerant Perception System for Autonomous Vehicles Type Journal Article
  Year 2016 Publication Journal of Automation and Control Engineering (JOACE) Abbreviated Journal  
  Volume 4 Issue Pages 430-436  
  Keywords Fault Tolerance, Data Fusion, Multi-sensor Fusion, Autonomous Vehicles, Perception System  
  Abstract Driverless vehicles are currently being tested on public roads in order to examine their ability to perform in a safe and reliable way in real world situations. However, the long-term reliable operation of a vehicle’s diverse sensors and the effects of potential sensor faults in the vehicle system have not been tested yet. This paper is proposing a sensor fusion architecture that minimizes the influence of a sensor fault. Experimental results are presented simulating faults by introducing displacements in the sensor information from the KITTI dataset.  
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  Language English Summary Language English Original Title  
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  Area Expedition Conference  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial 51  
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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 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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  Language Enlgish Summary Language English Original Title  
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  Area Expedition Conference  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial 54  
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Author Marjorie Chalen; Boris X. Vintimilla pdf  openurl
  Title Towards Action Prediction Applying Deep Learning Type Journal Article
  Year 2019 Publication Latin American Conference on Computational Intelligence (LA-CCI); Guayaquil, Ecuador; 11-15 Noviembre 2019 Abbreviated Journal  
  Volume Issue Pages  
  Keywords action prediction, early recognition, early detec- tion, action anticipation, cnn, deep learning, rnn, lstm.  
  Abstract Considering the incremental development future action prediction by video analysis task of computer vision where it is done based upon incomplete action executions. Deep learning is playing an important role in this task framework. Thus, this paper describes recently techniques and pertinent datasets utilized in human action prediction task.  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 129  
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Author Santos V.; Angel D. Sappa.; Oliveira M. & de la Escalera A. pdf  openurl
  Title Special Issue on Autonomous Driving and Driver Assistance Systems Type Journal Article
  Year 2019 Publication In Robotics and Autonomous Systems Abbreviated Journal  
  Volume Vol. 121 Issue Pages  
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  Call Number gtsi @ user @ Serial 119  
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Author Cristhian A. Aguilera, Cristhian Aguilera, Cristóbal A. Navarro, & Angel D. Sappa pdf  openurl
  Title Fast CNN Stereo Depth Estimation through Embedded GPU Devices Type Journal Article
  Year 2020 Publication Sensors 2020 Abbreviated Journal  
  Volume Vol. 2020-June Issue 11 Pages pp. 1-13  
  Keywords stereo matching; deep learning; embedded GPU  
  Abstract Current CNN-based stereo depth estimation models can barely run under real-time

constraints on embedded graphic processing unit (GPU) devices. Moreover, state-of-the-art

evaluations usually do not consider model optimization techniques, being that it is unknown what is

the current potential on embedded GPU devices. In this work, we evaluate two state-of-the-art models

on three different embedded GPU devices, with and without optimization methods, presenting

performance results that illustrate the actual capabilities of embedded GPU devices for stereo depth

estimation. More importantly, based on our evaluation, we propose the use of a U-Net like architecture

for postprocessing the cost-volume, instead of a typical sequence of 3D convolutions, drastically

augmenting the runtime speed of current models. In our experiments, we achieve real-time inference

speed, in the range of 5–32 ms, for 1216  368 input stereo images on the Jetson TX2, Jetson Xavier,

and Jetson Nano embedded devices.		  
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  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 14248220 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial 132  
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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.		  
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  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 133  
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