Listar por autor "López-Rubio, Ezequiel"
Mostrando ítems 1-20 de 96
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A Convolutional Autoencoder and a Neural Gas model based on Bregman Divergences for Hierarchical Color Quantization
Fernández-Rodríguez, Jose David; Palomo-Ferrer, Esteban José
; Benito-Picazo, Jesús; Domínguez-Merino, Enrique; López-Rubio, Ezequiel; Ortega-Zamorano, Francisco[et al.] (Elsevier, 2023)
Color quantization (CQ) is one of the most common and important procedures to be performed on digital images. In this paper, a new approach to hierarchical color quantization is described, presenting a novel neural network ... -
A fast robust geometric fitting method for parabolic curves.
López-Rubio, Ezequiel; Thurnhofer-Hemsi, Karl; Blázquez-Parra, Elidia Beatriz; De-Cózar-Macías, Óscar; Ladrón-de-Guevara-Muñoz, María del Carmen (Elsevier, 2018-07-18)
Fitting discrete data obtained by image acquisition devices to a curve is a common task in many fields of science and engineering. In particular, the parabola is some of the most employed shape features in electrical ... -
A novel continual learning approach for competitive neural networks
Fernández-Rodríguez, Jose David; Maza Quiroga, Rosa María; Palomo-Ferrer, Esteban José; Ortiz-de-Lazcano-Lobato, Juan Miguel; López-Rubio, Ezequiel (2022)
Continual learning tries to address the stability-plasticity dilemma to avoid catastrophic forgetting when dealing with non-stationary distributions. Prior works focused on supervised or reinforcement learning, but few ... -
Analysis of functional connectome pipelines for the diagnosis of autism spectrum disorders
Maza Quiroga, Rosa María; López-Rodríguez, Domingo; Thurnhofer-Hemsi, Karl; Luque-Baena, Rafael Marcos; Jiménez Valverde, Clara; López-Rubio, Ezequiel[et al.] (2022-05)
This paper explores the effect of using different pipelines to compute connectomes (matrices representing brain connections) and use them to train machine learning models with the goal of diagnosing Autism Spectrum ... -
Anomalous trajectory detection for automated traffic video surveillance
Fernández-Rodríguez, Jose David; García-González, Jorge; Benítez-Rochel, Rafaela; Molina-Cabello, Miguel Ángel; López-Rubio, Ezequiel; García-González, Jorge[et al.] (2022)
Vehicle trajectories extracted from traffic video sequences can be helpful for many purposes. In particular, the analysis of detected anomalous trajectories may enhance drivers’ safety. This work proposes a methodology to ... -
Aplicación web para la gestión automática de publicaciones de grupos de investigación.
(2024)La tarea de los investigadores en cualquier campo de la ciencia da lugar a una gran cantidad de publicaciones en foros de divulgación, revistas, congresos de reconocido prestigio para dar a conocer su trabajo durante ... -
Automate d lab eling of training data for improved object detection in traffic videos by fine-tuned deep convolutional neural networks
García Aguilar, Iván; García-González, Jorge; Luque-Baena, Rafael Marcos; López-Rubio, Ezequiel (Elsevier, 2023)
The exponential increase in the use of technology in road management systems has led to real-time vi- sual information in thousands of locations on road networks. A previous step in preventing or detecting accidents involves ... -
Automated detection of vehicles with anomalous trajectories in traffic surveillance videos.
Fernández-Rodríguez, Jose David; García-González, Jorge; Benítez-Rochel, Rafaela; Molina-Cabello, Miguel Ángel; Ramos-Jiménez, Gonzalo Pascual; López-Rubio, Ezequiel[et al.] (IOS Press, 2023-05-10)
Video feeds from traffic cameras can be useful for many purposes, the most critical of which are related to monitoring road safety. Vehicle trajectory is a key element in dangerous behavior and traffic accidents. In this ... -
Background modeling by shifted tilings of stacked denoising autoencoders
García-González, Jorge; Ortiz-de-Lazcano-Lobato, Juan Miguel; Luque-Baena, Rafael Marcos; López-Rubio, Ezequiel (2019-06-18)
The effective processing of visual data without interruption is currently of supreme importance. For that purpose, the analysis system must adapt to events that may affect the data quality and maintain its performance level ... -
Background modeling for video sequences by stacked denoising autoencoders
García-González, Jorge; Ortiz-de-Lazcano-Lobato, Juan Miguel; Luque-Baena, Rafael Marcos; Molina-Cabello, Miguel Ángel; López-Rubio, Ezequiel (2018-11-05)
Nowadays, the analysis and extraction of relevant information in visual data flows is of paramount importance. These images sequences can last for hours, which implies that the model must adapt to all kinds of circumstances ... -
Background subtraction by probabilistic modeling of patch features learned by deep autoencoders.
García-González, Jorge; Ortiz-de-Lazcano-Lobato, Juan Miguel; Luque-Baena, Rafael Marcos; López-Rubio, Ezequiel (2020-08-01)
Video sequence analysis systems must be able to operate for long periods of time and they must attempt that the different events that can affect the quality of the input data do not diminish the performance of the system ... -
Blood Cell Classification Using the Hough Transform and Convolutional Neural Networks
Molina-Cabello, Miguel Ángel; López-Rubio, Ezequiel; Luque-Baena, Rafael Marcos; Rodríguez-Espinosa, María Jesús; Thurnhofer-Hemsi, Karl (Springer, 2018)
The detection of red blood cells in blood samples can be crucial for the disease detection in its early stages. The use of image processing techniques can accelerate and improve the effectiveness and efficiency of this ... -
Clasificación de imágenes de lesiones cutáneas mediante redes neuronales convolucionales y aprendizaje profundo
(2020-11-24)El cáncer de piel es el cuarto tipo de cáncer más común en el mundo, el primero en numerosos países y, además, se espera que su incidencia aumente en las próximas décadas. Gran parte de ellos presentan una mortalidad ... -
Clasificación de noticias mediante técnicas de procesamiento del lenguaje natural basadas en aprendizaje profundo.
(2021-06)Uno de los sectores más afectados por la evolución tecnológica de los últimos años es el periodístico, debido tanto al auge del periodismo digital como a la personalización de contenidos mediante el estudio de perfiles de ... -
Color Space Selection for Self-Organizing Map Based Foreground Detection in Video Sequences
López-Rubio, Francisco Javier; López-Rubio, Ezequiel; Luque-Baena, Rafael Marcos; Domínguez, Enrique; Palomo-Ferrer, Esteban José (2014-07-18)
The selection of the best color space is a fundamental task in detecting foreground objects on scenes. In many situations, especially on dynamic backgrounds, neither grayscale nor RGB color spaces represent the best solution ... -
Content Based Image Retrieval by Convolutional Neural Networks
Hamreras, Safa; Benítez-Rochel, Rafaela; Boucheham, Bachir; Molina-Cabello, Miguel Ángel; López-Rubio, Ezequiel (2019-06-07)
In this paper, we present a Convolutional Neural Network (CNN) for feature extraction in Content based Image Retrieval (CBIR). The proposed CNN aims at reducing the semantic gap between low level and high-level features. ... -
Content-based image retrieval by ensembles of deep learning object classifiers.
Hamreras, Safa; Boucheham, Bachir; Molina-Cabello, Miguel Ángel; Benítez-Rochel, Rafaela; López-Rubio, Ezequiel (2020-05-20)
Ensemble learning has demonstrated its efficiency in many computer vision tasks. In this paper, we address this paradigm within content based image retrieval (CBIR). We propose to build an ensemble of convolutional neural ... -
Continuous Chemical Classification in Uncontrolled Environments with Sliding Windows.
González-Monroy, Javier; Palomo-Ferrer, Esteban José; López-Rubio, Ezequiel; Gonzalez-Jimenez, Antonio Javier (Elsevier, 2016-09-03)
Electronic noses are sensing devices able to classify chemical volatiles according to the readings of an array of non-selective gas sensors and some pattern recognition algorithm. Given their high versatility to host ... -
Deep Learning networks with p-norm loss layers for spatial resolution enhancement of 3D medical images
Thurnhofer-Hemsi, Karl; López-Rubio, Ezequiel; Roé-Vellvé, Núria; Molina-Cabello, Miguel Ángel (2019-06-19)
Nowadays, obtaining high-quality magnetic resonance (MR) images is a complex problem due to several acquisition factors, but is crucial in order to perform good diagnostics. The enhancement of the resolution is a typical ... -
Deep Learning Neural Networks to Improve Small Object Detection.
(UMA Editorial, 2024)This Ph.D. thesis is about enhancing small object detection and segmentation in road video sequences by integrating convolutional neural networks (CNNs) and super-resolution (SR) techniques. In response to the increasing ...