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Author Gisel Bastidas G., Patricio Moreno V., Boris Vintimilla & Angel D. Sappa openurl 
  Title Application-Guided Image Fusion: A Path to Improve Results in High-Level Vision Tasks Type Journal Article
  Year 2025 Publication 20th International Conference on Computer Vision Theory and Applications VISAPP 2025 Abbreviated Journal  
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  Call Number cidis @ cidis @ Serial 266  
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Author Henry O. Velesaca, Angel D. Sappa & Juan A. Holgado openurl 
  Title A Case Study of Anomaly Detection in Tinplate Lids: Supervised vs Unsupervised approaches Type Journal Article
  Year 2025 Publication 11th International Conference on Automation, Robotics, and Applications (ICARA 2025) Abbreviated Journal  
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  Call Number cidis @ cidis @ Serial 267  
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Author Henry O. Velesaca & Angel D. Sappa openurl 
  Title Seeing the Unseen: AI-Powered Camouflaged Pest Detection Type Journal Article
  Year 2025 Publication 9th International Conference on Machine Vision and Information Technology (CMVIT 2025) Abbreviated Journal  
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  Call Number cidis @ cidis @ Serial 268  
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Author Constantine Macías A., Toala Paz A., Realpe Miguel, Suárez Moncada Jenifer, Páez Rosas Diego & Jarrín Enrique Peláez openurl 
  Title Leveraging Deep Learning Techniques for Marine and Coastal Wildlife Using Instance Segmentation: A Study on Galápagos Sea Lions Type Journal Article
  Year 2024 Publication In 8th Ecuador Technical Chapters Meeting (ETCM 2024) Cuenca, October 15 – October 18, 2024 Abbreviated Journal  
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  Call Number cidis @ cidis @ Serial 269  
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Author Leo Thomas Ramos & Angel D. Sappa pdf  openurl
  Title Leveraging U-Net and selective feature extraction for land cover classification using remote sensing imagery Type Journal Article
  Year 2025 Publication Scientific Reports Abbreviated Journal  
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  Call Number cidis @ cidis @ Serial 270  
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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 Vol. 84 Issue Pages pp. 113-128  
  Keywords Scene reconstruction, Autonomous driving, Texture mapping  
  Abstract (up) 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 Ma. Paz Velarde; Erika Perugachi; Dennis G. Romero; Ángel D. Sappa; Boris X. Vintimilla pdf  url
openurl 
  Title Análisis del movimiento de las extremidades superiores aplicado a la rehabilitación física de una persona usando técnicas de visión artificial. Type Journal Article
  Year 2015 Publication Revista Tecnológica ESPOL-RTE Abbreviated Journal  
  Volume Vol. 28 Issue Pages pp. 1-7  
  Keywords Rehabilitation; RGB-D Sensor; Computer Vision; Upper limb  
  Abstract (up) Comúnmente durante la rehabilitación física, el diagnóstico dado por el especialista se basa en observaciones cualitativas que sugieren, en algunos casos, conclusiones subjetivas. El presente trabajo propone un enfoque cuantitativo, orientado a servir de ayuda a fisioterapeutas, a través de una herramienta interactiva y de bajo costo que permite medir los movimientos de miembros superiores. Estos movimientos son capturados por un sensor RGB-D y procesados mediante la metodología propuesta, dando como resultado una eficiente representación de movimientos, permitiendo la evaluación cuantitativa de movimientos de los miembros superiores.  
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  Call Number cidis @ cidis @ Serial 39  
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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 pp. 1-3  
  Keywords action prediction, early recognition, early detec- tion, action anticipation, cnn, deep learning, rnn, lstm.  
  Abstract (up) 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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  Call Number cidis @ cidis @ Serial 129  
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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 (up) 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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  Call Number cidis @ cidis @ Serial 132  
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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 Vol. 4 Issue Pages pp. 430-436  
  Keywords Fault Tolerance, Data Fusion, Multi-sensor Fusion, Autonomous Vehicles, Perception System  
  Abstract (up) 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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  Call Number cidis @ cidis @ Serial 51  
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