• Content Based Image Retrieval by Convolutional Neural Networks 

      Hamreras, Safa; Benitez-Rochel, Rafaela; Boucheham, Bachir; Molina-Cabello, Miguel A.; 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 A. (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 ...
    • Encoding generative adversarial networks for defense against image classification attacks 

      Rodríguez Rodríguez, José Antonio; Pérez Bravo, José María; García González, Jorge; Molina-Cabello, Miguel A.; Thurnhofer-Hemsi, Karl; [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 ...
    • Foreground object detection enhancement by adaptive super resolution for video surveillance 

      Molina-Cabello, Miguel A.; Elizondo, David A.; 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 ...
    • Homography estimation with deep convolutional neural networks by random color transformations 

      Molina-Cabello, Miguel A.; Elizondo, David A.; 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; [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 ...
    • Infering Air Quality from Traffic Data using Transferable Neural Network Models 

      Molina-Cabello, Miguel A.; Passow, Benjamin N.; Domínguez-Merino, Enrique; Elizondo, David A.; Obszynska, Jolanta (Springer, 2019-06)
      This work presents a neural network based model for inferring air quality from traffic measurements. It is important to obtain information on air quality in urban environments in order to meet legislative and policy ...
    • Longitudinal study of the learning styles evolution in Engineering degrees 

      Molina-Cabello, Miguel A.; Thurnhofer-Hemsi, Karl; Domínguez, Enrique; López-Rubio, Ezequiel; Palomo, Esteban J. (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 ...
    • Neural Controller for PTZ cameras based on nonpanoramic foreground detection 

      Molina-Cabello, Miguel A.; 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, ...
    • A new self-organizing neural gas model based on Bregman divergences 

      Palomo, Esteban J.; Molina-Cabello, Miguel A.; López-Rubio, Ezequiel; Luque-Baena, Rafael Marcos (2018-07-20)
      In this paper, a new self-organizing neural gas model that we call Growing Hierarchical Bregman Neural Gas (GHBNG) has been proposed. Our proposal is based on the Growing Hierarchical Neural Gas (GHNG) in which Bregman ...
    • Optimization of Convolutional Neural Network ensemble classifiers by Genetic Algorithms 

      Molina-Cabello, Miguel A.; Accino, Cristian; López-Rubio, Ezequiel; Thurnhofer-Hemsi, Karl (Springer, 2019)
      Breast cancer exhibits a high mortality rate and it is the most invasive cancer in women. An analysis from histopathological images could predict this disease. In this way, computational image processing might support this ...
    • Panoramic Background Modeling for PTZ Cameras with Competitive Learning Neural Networks 

      Thurnhofer-Hemsi, Karl; López-Rubio, Ezequiel; Domínguez, Enrique; Luque-Baena, Rafael Marcos; Molina-Cabello, Miguel A. (2017-05-29)
      The construction of a model of the background of a scene still remains as a challenging task in video surveillance systems, in particular for moving cameras. This work presents a novel approach for constructing a panoramic ...
    • Peer assessments in Engineering: A pilot project 

      Thurnhofer-Hemsi, Karl; Molina-Cabello, Miguel A.; Palomo, Esteban J.; López-Rubio, Ezequiel; Domínguez, Enrique (2021)
      The evaluation methods employed in a course are the most important point for the students, above any other learning aspect. For teachers, this task is arduous when the number of students is high. Traditional evaluation ...
    • Road pollution estimation using static cameras and neural networks 

      Molina-Cabello, Miguel A.; Luque-Baena, Rafael Marcos; López-Rubio, Ezequiel; Deka, lipika; Thurnhofer-Hemsi, Karl (2018-07-19)
      Este artículo presenta una metodología para estimar la contaminación en carreteras mediante el análisis de secuencias de video de tráfico. El objetivo es aprovechar la gran red de cámaras IP existente en el sistema de ...
    • Vehicle Type Detection by Convolutional Neural Networks 

      Molina-Cabello, Miguel A.; Luque-Baena, Rafael Marcos; López-Rubio, Ezequiel; Thurnhofer-Hemsi, Karl (Springer, 2017)
      In this work a new vehicle type detection procedure for traffic surveillance videos is proposed. A Convolutional Neural Network is integrated into a vehicle tracking system in order to accomplish this task. Solutions for ...