Listar por autor "López-Rubio, Ezequiel"
Mostrando ítems 41-60 de 96
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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 detection by competitive learning for varying input distributions
López-Rubio, Ezequiel; Molina-Cabello, Miguel Ángel; Luque-Baena, Rafael Marcos; Domínguez-Merino, Enrique (World Scientific Publishing, 2018)
One of the most important challenges in computer vision applications is the background modeling, especially when the background is dynamic and the input distribution might not be stationary, i.e. the distribution of the ... -
Foreground detection by probabilistic modeling of the features discovered by stacked denoising autoencoders in noisy video sequences
García-González, Jorge; Ortiz-de-Lazcano-Lobato, Juan Miguel; Luque-Baena, Rafael Marcos; Molina-Cabello, Miguel Ángel; López-Rubio, Ezequiel (2019-06-07)
A robust foreground detection system is presented, which is resilient to noise in video sequences. The proposed model divides each video frame in patches that are fed to a stacked denoising autoencoder, which is responsible ... -
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 ... -
Identificación automática de orgánulos celulares mediante redes neuronales convolucionales de aprendizaje profundo
(2021-09)Las proteínas en nuestro organismo son las encargadas de formar tejidos, transportar sustancias y defender al organismo contra infecciones o agentes patógenos, entre otras funciones. Conociendo la ubicación y el transporte ... -
Image segmentation with the growing neural gas
(2017-01-31)Over many years algorithms have been proposed as methods for the segmentation of images. Everytime the work has been improved and optimized for better results with much faster algorithms and newer ways to adapt the ... -
Improved detection of small objects in road network sequences using CNN and super resolution
The detection of small objects is one of the problems present in deep learning due to the context of the scene or the low number of pixels of the objects to be detected. According to these problems, current pre-trained ... -
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 ... -
Latent diffusion for arbitrary zoom MRI super-resolution.
Mármol-Rivera, Jorge Andrés; Fernández-Rodríguez, Jose David; Asenjo García, Beatriz; López-Rubio, Ezequiel (Elsevier, 2025)
In various image processing tasks, enhancing resolution is a fundamental challenge, particularly along specific axes where resolution tends to be lower. This limitation can hinder the performance of models in tasks such ... -
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 ... -
Mitigating Carlini & Wagner attacks with Encoding Generative Adversarial Network.
Tell-Gónzalez, Guillermo; Fernández-Rodríguez, Jose David; Molina-Cabello, Miguel Ángel; Benítez-Rochel, Rafaela; López-Rubio, Ezequiel (2024)
Deep Learning models are experiencing a significant surge in popularity, expanding into various domains, including critical applications like object recognition in autonomous vehicles, where any failure could have fatal ... -
Modelado de temas para el análisis de la similitud entre usuarios en Twitter
(2018-03-06)La minería de datos en redes sociales está ganando importancia debido a que permite realizar campañas de marketing más precisas. Por ejemplo, Google realiza un análisis de todos nuestros datos: vídeos que vemos, términos ... -
Modelado de temas para el análisis de la similitud entre usuarios en Twitter
(2018-03-22)La minería de datos en redes sociales está ganando importancia debido a que permite realizar campañas de marketing más precisas. Por ejemplo, Google realiza un análisis de todos nuestros datos: vídeos que vemos, términos ... -
Motion Detection by Microcontroller for Panning Cameras
Benito-Picazo, Jesús; López-Rubio, Ezequiel; Ortiz-de-Lazcano-Lobato, Juan Miguel; Domínguez-Merino, Enrique; Palomo-Ferrer, Esteban José (2017-07-24)
Motion detection is the first essential process in the extraction of information regarding moving objects. The approaches based on background difference are the most used with fixed cameras to perform motion detection, ... -
Moving object detection in noisy video sequences using deep convolutional disentangled representations.
García-González, Jorge; Luque-Baena, Rafael Marcos; Ortiz-de-Lazcano-Lobato, Juan Miguel; López-Rubio, Ezequiel (2022)
Noise robustness is crucial when approaching a moving de- tection problem since image noise is easily mistaken for movement. In order to deal with the noise, deep denoising autoencoders are commonly proposed to be applied ... -
Multiobjective optimization of deep neural networks with combinations of Lp-norm cost functions for 3D medical image super-resolution
Thurnhofer-Hemsi, Karl; López-Rubio, Ezequiel; Roé-Vellvé, Núria; Molina-Cabello, Miguel Ángel (IOS Press, 2020-05-20)
In medical imaging, the lack of high-quality images is present in many areas such as magnetic resonance (MR). Due to many acquisition impediments, the generated images have not enough resolution to carry out an adequate ... -
Neural Controller for PTZ cameras based on nonpanoramic foreground detection
Molina-Cabello, Miguel Ángel; López-Rubio, Ezequiel; Luque-Baena, Rafael Marcos; Domínguez, Enrique; Thurnhofer-Hemsi, Karl (2017-05-29)
Abstract—In this paper a controller for PTZ cameras based on an unsupervised neural network model is presented. It takes advantage of the foreground mask generated by a nonparametric foreground detection subsystem. Thus, ...