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Listar LCC - Contribuciones a congresos científicos por autor "López-Rubio, Ezequiel"
Mostrando ítems 1-20 de 34
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A novel continual learning approach for competitive neural networks
Fernández Rodríguez, José 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, José 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 ... -
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 ... -
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 ... -
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. ... -
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-based anomalous object detection system for panoramic cameras managed by a Jetson TX2 board
Benito-Picazo, Jesús; Domínguez-Merino, Enrique; Palomo-Ferrer, Esteban José; Ramos-Jiménez, Gonzalo Pascual; López-Rubio, Ezequiel (IEEE, 2021)Social conflicts appearing in the media are increas ing public awareness about security issues, resulting in a higher demand of more exhaustive environment monitoring methods. Automatic video surveillance systems are a ... -
Deep learning-based anomalous object detection system powered by microcontroller for PTZ cameras
Benito Picazo, Jesús; Domínguez-Merino, Enrique; Palomo-Ferrer, Esteban José; López-Rubio, Ezequiel; Ortiz-de-Lazcano-Lobato, Juan Miguel (IEEE, 2018)Automatic video surveillance systems are usually designed to detect anomalous objects being present in a scene or behaving dangerously. In order to perform adequately, they must incorporate models able to achieve accurate ... -
Encoding generative adversarial networks for defense against image classification attacks
Rodríguez Rodríguez, José Antonio; Pérez Bravo, José María; García-González, Jorge; Molina-Cabello, Miguel Ángel; Thurnhofer-Hemsi, Karl; López-Rubio, Ezequiel[et al.] (2022)Image classification has undergone a revolution in recent years due to the high performance of new deep learning models. However, severe security issues may impact the performance of these systems. In particular, adversarial ... -
Enhanced Perspective Generation by Consensus of NeX neural models
Pacheco dos Santos Lima Junior, Marcos Sergio; Fernández Rodríguez, José David; Ortiz-de-Lazcano-Lobato, Juan Miguel; López-Rubio, Ezequiel; Domínguez-Merino, Enrique (2022-07)Neural rendering is a relatively new field of research that aims to produce high quality perspectives of a 3D scene from a reduced set of sample images. This is done with the help of deep artificial neural networks that ... -
Enhanced transfer learning model by image shifting on a square lattice for skin lesion malignancy assessment
Molina-Cabello, Miguel Ángel; Thurnhofer Hemsi, Karl; Maza Quiroga, Rosa María; Domínguez, Enrique; López-Rubio, Ezequiel; Molina-Cabello, Miguel Ángel[et al.] (2021)Skin cancer is one of the most prevalent diseases among people. Physicians have a challenge every time they have to determine whether a diseased skin is benign or malign. There exist clinical diagnosis methods (such as ... -
Feature density as an uncertainty estimator method in the binary classification mammography images task for a supervised deep learning model
Hernández Vásquez, Marco A.; Fuentes Fino, Ricardo Javier; Calderón-Ramírez, Saúl; Domínguez-Merino, Enrique; López-Rubio, Ezequiel; Molina-Cabello, Miguel Ángel[et al.] (2022)Labeled medical datasets may include a limited number of observations for each class, while unlabeled datasets may include observations from patients with pathologies other than those observed in the labeled dataset. This ... -
Foreground object detection enhancement by adaptive super resolution for video surveillance
Molina-Cabello, Miguel Ángel; Elizondo Acuña, David Alberto; Luque-Baena, Rafael Marcos; López-Rubio, Ezequiel (2019-09-16)Foreground object detection is a fundamental low level task in current video surveillance systems. It is usually accomplished by keeping a model of the background at each frame pixel. Many background learning algorithms ... -
Hierarchical Color Quantization with a Neural Gas Model Based on Bregman Divergences
Palomo-Ferrer, Esteban José; Benito Picazo, Jesús; Domínguez-Merino, Enrique; López-Rubio, Ezequiel; Ortega-Zamorano, Francisco (Springer, 2021-09)In this paper, a new color quantization method based on a self-organized artificial neural network called the Growing Hierarchical Bregman Neural Gas (GHBNG) is proposed. This neural network is based on Bregman divergences, ... -
Histopathological image analysis for breast cancer diagnosis by ensembles of convolutional neural networks and genetic algorithms
Molina-Cabello, Miguel Ángel; Rodríguez Rodríguez, José Antonio; Thurnhofer Hemsi, Karl; López-Rubio, Ezequiel (2021-07)One of the most invasive cancer types which affect women is breast cancer. Unfortunately, it exhibits a high mortality rate. Automated histopathological image analysis can help to diagnose the disease. Therefore, computer ... -
Homography estimation with deep convolutional neural networks by random color transformations
Molina-Cabello, Miguel Ángel; Elizondo Acuña, David Alberto; Luque-Baena, Rafael Marcos; López-Rubio, Ezequiel (2019-09-13)Most classic approaches to homography estimation are based on the filtering of outliers by means of the RANSAC method. New proposals include deep convolutional neural networks. Here a new method for homography estimation ... -
Improving Uncertainty Estimations for Mammogram Classification using Semi-Supervised Learning
Calderón-Ramírez, Saúl; Murillo-Hernández, Diego; Rojas-Salazar, Kevin; Calvo-Valverde, Luis-Alexander; Yang, Shengxiang; Moemeni, Armaghan; Elizondo Acuña, David Alberto; López-Rubio, Ezequiel; Molina-Cabello, Miguel Ángel[et al.] (2021-07)Computer aided diagnosis for mammogram images have seen positive results through the usage of deep learning architectures. However, limited sample sizes for the target datasets might prevent the usage of a deep learning ... -
Longitudinal study of the learning styles evolution in Engineering degrees
Molina-Cabello, Miguel Ángel; Thurnhofer-Hemsi, Karl; Domínguez, Enrique; López-Rubio, Ezequiel; Palomo-Ferrer, Esteban José (2021)A learning style describes what are the predominant skills for learning tasks. In the context of university education, knowing the learning styles of the students constitutes a great opportunity to improve both teaching ...