Frame Size Reduction for Foreground Detection in Video Sequences. - Miguel A. Molina-Cabello; Ezequiel López-Rubio; Rafael Marcos Luque Baena; Esteban J. Palomo; Enrique Domínguez (CAEPIA, 2016)

Pixel Features for Self-organizing Map Based Detection of Foreground Objects in Dynamic Environments. - Miguel A. Molina-Cabello; Ezequiel López-Rubio; Rafael Marcos Luque Baena; Enrique Domínguez; Esteban J. Palomo (SOCO-CISIS-ICEUTE, 2016)

Smart motion detection sensor based on video processing using self-organizing maps. - Francisco Ortega-Zamorano; Miguel A. Molina-Cabello; Ezequiel López-Rubio; Esteban J. Palomo (Expert Syst. Appl., 2016)

Neural controller for PTZ cameras based on nonpanoramic foreground detection. - Miguel A. Molina-Cabello; Ezequiel López-Rubio; Rafael Marcos Luque Baena; Enrique Domínguez; Karl Thurnhofer-Hemsi (IJCNN, 2017)

Panoramic background modeling for PTZ cameras with competitive learning neural networks. - Karl Thurnhofer-Hemsi; Ezequiel López-Rubio; Enrique Domínguez; Rafael Marcos Luque Baena; Miguel A. Molina-Cabello (IJCNN, 2017)

Vehicle Classification in Traffic Environments Using the Growing Neural Gas. - Miguel A. Molina-Cabello; Rafael Marcos Luque Baena; Ezequiel López-Rubio; Juan Miguel Ortiz-de-Lazcano-Lobato; Enrique Domínguez; José Muñoz-Pérez (IWANN (2), 2017)

Vehicle Type Detection by Convolutional Neural Networks. - Miguel A. Molina-Cabello; Rafael Marcos Luque Baena; Ezequiel López-Rubio; Karl Thurnhofer-Hemsi (IWINAC (2), 2017)

A Growing Neural Gas Approach to Classify Vehicles in Traffic Environments. - Miguel A. Molina-Cabello; Rafael M. Luque-Baena; Ezequiel López-Rubio; Juan Miguel Ortiz-de-Lazcano-Lobato; Enrique Domínguez (Int. J. Comput. Vis. Image Process., 2017)

Background Modeling for Video Sequences by Stacked Denoising Autoencoders. - Jorge García-González; Juan Miguel Ortiz-de-Lazcano-Lobato; Rafael M. Luque-Baena; Miguel A. Molina-Cabello; Ezequiel López-Rubio (CAEPIA, 2018)

Road Pollution Estimation Using Static Cameras And Neural Networks. - Miguel A. Molina-Cabello; Rafael Marcos Luque Baena; Ezequiel López-Rubio; Lipika Deka; Karl Thurnhofer-Hemsi (IJCNN, 2018)

A New Self-Organizing Neural Gas Model based on Bregman Divergences. - Esteban J. Palomo; Miguel A. Molina-Cabello; Ezequiel López-Rubio; Rafael Marcos Luque Baena (IJCNN, 2018)

Super-resolution of 3D Magnetic Resonance Images by Random Shifting and Convolutional Neural Networks. - Karl Thurnhofer-Hemsi; Ezequiel López-Rubio; Núria Roé-Vellvé; Enrique Domínguez; Miguel A. Molina-Cabello (IJCNN, 2018)

Foreground Detection Enhancement Using Pearson Correlation Filtering. - Rafael Marcos Luque Baena; Miguel A. Molina-Cabello; Ezequiel López-Rubio; Enrique Domínguez (IPMU (3), 2018)

Blood Cell Classification Using the Hough Transform and Convolutional Neural Networks. - Miguel A. Molina-Cabello; Ezequiel López-Rubio; Rafael M. Luque-Baena; María Jesús Rodríguez-Espinosa; Karl Thurnhofer-Hemsi (WorldCIST (2), 2018)

The effect of noise on foreground detection algorithms. - Francisco Javier López-Rubio; Ezequiel López-Rubio; Miguel A. Molina-Cabello; Rafael Marcos Luque Baena; Esteban J. Palomo; Enrique Domínguez (Artif. Intell. Rev., 2018)

Panorama construction for PTZ camera surveillance with the neural gas network. - Karl Thurnhofer-Hemsi; Ezequiel López-Rubio; Enrique Domínguez; Rafael Marcos Luque Baena; Miguel A. Molina-Cabello (Expert Syst. J. Knowl. Eng., 2018)

Vehicle type detection by ensembles of convolutional neural networks operating on super resolved images. - Miguel A. Molina-Cabello; Rafael Marcos Luque Baena; Ezequiel López-Rubio; Karl Thurnhofer-Hemsi (Integr. Comput. Aided Eng., 2018)

Foreground object detection for video surveillance by fuzzy logic based estimation of pixel illumination states. - Miguel A. Molina-Cabello; Ezequiel López-Rubio; Rafael M. Luque-Baena; Enrique Domínguez; Esteban J. Palomo (Log. J. IGPL, 2018)

Foreground Detection by Competitive Learning for Varying Input Distributions. - Ezequiel López-Rubio; Miguel A. Molina-Cabello; Rafael Marcos Luque Baena; Enrique Domínguez (Int. J. Neural Syst., 2018)

Cooperative Evaluation Using Moodle. - Enrique Domínguez; Ezequiel López-Rubio; Miguel A. Molina-Cabello (CISIS-ICEUTE, 2019)

Optimization of Convolutional Neural Network Ensemble Classifiers by Genetic Algorithms. - Miguel A. Molina-Cabello; Cristian Accino; Ezequiel López-Rubio; Karl Thurnhofer-Hemsi (IWANN (2), 2019)

Infering Air Quality from Traffic Data Using Transferable Neural Network Models. - Miguel A. Molina-Cabello; Benjamin N. Passow; Enrique Domínguez; David A. Elizondo; Jolanta Obszynska (IWANN (1), 2019)

Content Based Image Retrieval by Convolutional Neural Networks. - Safa Hamreras; Rafaela Benítez-Rochel; Bachir Boucheham; Miguel A. Molina-Cabello; Ezequiel López-Rubio (IWINAC (2), 2019)

Deep Learning Networks with p-norm Loss Layers for Spatial Resolution Enhancement of 3D Medical Images. - Karl Thurnhofer-Hemsi; Ezequiel López-Rubio; Núria Roé-Vellvé; Miguel A. Molina-Cabello (IWINAC (2), 2019)

Foreground detection by probabilistic modeling of the features discovered by stacked denoising autoencoders in noisy video sequences. - Jorge García-González; Juan Miguel Ortiz-de-Lazcano-Lobato; Rafael M. Luque-Baena; Miguel A. Molina-Cabello; Ezequiel López-Rubio (Pattern Recognit. Lett., 2019)

Deep Autoencoder Architectures For Foreground Object Detection In Video Sequences Based On Probabilistic Mixture Models. - Jorge García-González; Miguel A. Molina-Cabello; Rafael M. Luque-Baena; Juan Miguel Ortiz-de-Lazcano-Lobato; Ezequiel López-Rubio (ICIP, 2020)

Adaptive estimation of optimal color transformations for deep convolutional network based homography estimation. - Miguel A. Molina-Cabello; Jorge García-González; Rafael Marcos Luque Baena; Karl Thurnhofer-Hemsi; Ezequiel López-Rubio (ICPR, 2020)

Dealing with Scarce Labelled Data: Semi-supervised Deep Learning with Mix Match for Covid-19 Detection Using Chest X-ray Images. - Saúl Calderón Ramírez; Raghvendra Giri; Shengxiang Yang; Armaghan Moemeni; Mario Umaña; David A. Elizondo; Jordina Torrents-Barrena; Miguel A. Molina-Cabello (ICPR, 2020)

The Impact of Linear Motion Blur on the Object Recognition Efficiency of Deep Convolutional Neural Networks. - José A. Rodríguez-Rodríguez; Miguel A. Molina-Cabello; Rafaela Benítez-Rochel; Ezequiel López-Rubio (ICPR Workshops (6), 2020)

The Effect of Noise and Brightness on Convolutional Deep Neural Networks. - José A. Rodríguez-Rodríguez; Miguel A. Molina-Cabello; Rafaela Benítez-Rochel; Ezequiel López-Rubio (ICPR Workshops (6), 2020)

The effect of image enhancement algorithms on convolutional neural networks. - José A. Rodríguez-Rodríguez; Miguel A. Molina-Cabello; Rafaela Benítez-Rochel; Ezequiel López-Rubio (ICPR, 2020)

Performance of Deep Learning and Traditional Techniques in Single Image Super-Resolution of Noisy Images. - Karl Thurnhofer-Hemsi; Guillermo Ruiz-Álvarez; Rafael Marcos Luque Baena; Miguel A. Molina-Cabello; Ezequiel López-Rubio (ICPR Workshops (6), 2020)

Aggregation of Convolutional Neural Network Estimations of Homographies by Color Transformations of the Inputs. - Miguel A. Molina-Cabello; David A. Elizondo; Rafael Marcos Luque Baena; Ezequiel López-Rubio (IEEE Access, 2020)

The effect of downsampling-upsampling strategy on foreground detection algorithms. - Miguel A. Molina-Cabello; Jorge García-González; Rafael M. Luque-Baena; Ezequiel López-Rubio (Artif. Intell. Rev., 2020)

MixMOOD: A systematic approach to class distribution mismatch in semi-supervised learning using deep dataset dissimilarity measures. - Saúl Calderón Ramírez; Luis Oala; Jordina Torrents-Barrena; Shengxiang Yang; Armaghan Moemeni; Wojciech Samek; Miguel A. Molina-Cabello (CoRR, 2020)

Radial basis function kernel optimization for Support Vector Machine classifiers. - Karl Thurnhofer-Hemsi; Ezequiel López-Rubio; Miguel A. Molina-Cabello; Kayvan Najarian (CoRR, 2020)

Foreground detection by ensembles of random polygonal tilings. - Miguel A. Molina-Cabello; David A. Elizondo; Rafael M. Luque-Baena; Ezequiel López-Rubio (Expert Syst. Appl., 2020)

Content based image retrieval by ensembles of deep learning object classifiers. - Safa Hamreras; Bachir Boucheham; Miguel A. Molina-Cabello; Rafaela Benítez-Rochel; Ezequiel López-Rubio (Integr. Comput. Aided Eng., 2020)

Multiobjective optimization of deep neural networks with combinations of Lp-norm cost functions for 3D medical image super-resolution. - Karl Thurnhofer-Hemsi; Ezequiel López-Rubio; Núria Roé-Vellvé; Miguel A. Molina-Cabello (Integr. Comput. Aided Eng., 2020)

Longitudinal Study of the Learning Styles Evolution in Engineering Degrees. - Miguel A. Molina-Cabello; Karl Thurnhofer-Hemsi; Enrique Domínguez; Ezequiel López-Rubio; Esteban J. Palomo (CISIS-ICEUTE, 2021)

Peer Assessments in Engineering: A Pilot Project. - Karl Thurnhofer-Hemsi; Miguel A. Molina-Cabello; Esteban J. Palomo; Ezequiel López-Rubio; Enrique Domínguez (CISIS-ICEUTE, 2021)

Dynamic selection of classifiers for Content Based Image Retrieval. - Safa Hamreras; Bachir Boucheham; Miguel A. Molina-Cabello; Rafaela Benítez-Rochel; Ezequiel López-Rubio (IJCNN, 2021)

Histopathological image analysis for breast cancer diagnosis by ensembles of convolutional neural networks and genetic algorithms. - Miguel A. Molina-Cabello; José A. Rodríguez-Rodríguez; Karl Thurnhofer-Hemsi; Ezequiel López-Rubio (IJCNN, 2021)

Improving Uncertainty Estimations for Mammogram Classification using Semi-Supervised Learning. - Saúl Calderón Ramírez; Diego Murillo-Hernandez; Kevin Rojas-Salazar; Luis-Alexander Calvo-Valverde; Shengxiang Yang; Armaghan Moemeni; David A. Elizondo; Ezequiel López-Rubio; Miguel A. Molina-Cabello (IJCNN, 2021)

Test time augmentation by regular shifting for deep denoising autoencoder networks. - José A. Rodríguez-Rodríguez; Miguel A. Molina-Cabello; Rafaela Benítez-Rochel; Ezequiel López-Rubio (IJCNN, 2021)

Enhanced transfer learning model by image shifting on a square lattice for skin lesion malignancy assessment. - Karl Thurnhofer-Hemsi; Rosa Maza-Quiroga; Enrique Domínguez; Miguel A. Molina-Cabello; Ezequiel López-Rubio (IJCNN, 2021)

Classification of Images as Photographs or Paintings by Using Convolutional Neural Networks. - José Miguel López-Rubio; Miguel A. Molina-Cabello; Gonzalo Ramos-Jiménez; Ezequiel López-Rubio (IWANN (1), 2021)

Enforcing Morphological Information in Fully Convolutional Networks to Improve Cell Instance Segmentation in Fluorescence Microscopy Images. - Willard Zamora-Cárdenas; Mauro Mendez; Saúl Calderón Ramírez; Martin Vargas; Gerardo Monge; Steve Quirós; David A. Elizondo; Jordina Torrents-Barrena; Miguel A. Molina-Cabello (IWANN (1), 2021)

Fuzzy Logic Applied to System Monitors. - Noel Khan; David A. Elizondo; Lipika Deka; Miguel A. Molina-Cabello (IEEE Access, 2021)

Improving Uncertainty Estimation With Semi-Supervised Deep Learning for COVID-19 Detection Using Chest X-Ray Images. - Saúl Calderón Ramírez; Shengxiang Yang; Armaghan Moemeni; Simon Colreavy-Donnelly; David A. Elizondo; Luis Oala; Jorge Rodríguez-Capitán; Manuel Jiménez-Navarro; Ezequiel López-Rubio; Miguel A. Molina-Cabello (IEEE Access, 2021)

Road pollution estimation from vehicle tracking in surveillance videos by deep convolutional neural networks. - Jorge García-González; Miguel A. Molina-Cabello; Rafael M. Luque-Baena; Juan Miguel Ortiz-de-Lazcano-Lobato; Ezequiel López-Rubio (Appl. Soft Comput., 2021)

Correcting data imbalance for semi-supervised COVID-19 detection using X-ray chest images. - Saúl Calderón Ramírez; Shengxiang Yang; Armaghan Moemeni; David A. Elizondo; Simon Colreavy-Donnelly; Luis Fernando Chavarria-Estrada; Miguel A. Molina-Cabello (Appl. Soft Comput., 2021)

Enforcing Morphological Information in Fully Convolutional Networks to Improve Cell Instance Segmentation in Fluorescence Microscopy Images. - Willard Zamora-Cárdenas; Mauro Mendez; Saúl Calderón Ramírez; Martin Vargas; Gerardo Monge; Steve Quirós; David A. Elizondo; Miguel A. Molina-Cabello (CoRR, 2021)

A Real Use Case of Semi-Supervised Learning for Mammogram Classification in a Local Clinic of Costa Rica. - Saúl Calderón Ramírez; Diego Murillo-Hernandez; Kevin Rojas-Salazar; David A. Elizondo; Shengxiang Yang; Miguel A. Molina-Cabello (CoRR, 2021)

Anomalous object detection by active search with PTZ cameras. - Ezequiel López-Rubio; Miguel A. Molina-Cabello; Francisco M. Castro; Rafael M. Luque-Baena; Manuel J. Marín-Jiménez; Nicolás Guil (Expert Syst. Appl., 2021)

Feature Density as an Uncertainty Estimator Method in the Binary Classification Mammography Images Task for a Supervised Deep Learning Model. - Ricardo Javier Fuentes-Fino; Saúl Calderón Ramírez; Enrique Domínguez; Ezequiel López-Rubio; Marco A. Hernandez-Vasquez; Miguel A. Molina-Cabello (IWBBIO (2), 2022)

Anomalous Trajectory Detection for Automated Traffic Video Surveillance. - Jose D. Fernández 001; Jorge García-González; Rafaela Benítez-Rochel; Miguel A. Molina-Cabello; Ezequiel López-Rubio (IWINAC (1), 2022)

Encoding Generative Adversarial Networks for Defense Against Image Classification Attacks. - José M. Pérez-Bravo; José A. Rodríguez-Rodríguez; Jorge García-González; Miguel A. Molina-Cabello; Karl Thurnhofer-Hemsi; Ezequiel López-Rubio (IWINAC (1), 2022)

A real use case of semi-supervised learning for mammogram classification in a local clinic of Costa Rica. - Saúl Calderón Ramírez; Diego Murillo-Hernandez; Kevin Rojas-Salazar; David A. Elizondo; Shengxiang Yang; Armaghan Moemeni; Miguel A. Molina-Cabello (Medical Biol. Eng. Comput., 2022)

Automated detection of vehicles with anomalous trajectories in traffic surveillance videos. - Jose David Fernández Rodriguez; Jorge García-González; Rafaela Benítez-Rochel; Miguel A. Molina-Cabello; Gonzalo Ramos-Jiménez; Ezequiel López-Rubio (Integr. Comput. Aided Eng., 2023)

Dataset Similarity to Assess Semisupervised Learning Under Distribution Mismatch Between the Labeled and Unlabeled Datasets. - Saúl Calderón Ramírez; Luis Oala; Jordina Torrents-Barrena; Shengxiang Yang; David A. Elizondo; Armaghan Moemeni; Simon Colreavy-Donnelly; Wojciech Samek; Miguel A. Molina-Cabello; Ezequiel López-Rubio (IEEE Trans. Artif. Intell., 2023)

Are learning styles useful? A new software to analyze correlations with grades and a case study in engineering. - Miguel A. Molina-Cabello; Karl Thurnhofer-Hemsi; David Molina-Cabello; Esteban J. Palomo (Comput. Appl. Eng. Educ., 2023)

An uncertainty estimator method based on the application of feature density to classify mammograms for breast cancer detection. - Ricardo Javier Fuentes-Fino; Saúl Calderón Ramírez; Enrique Domínguez; Ezequiel López-Rubio; David A. Elizondo; Miguel A. Molina-Cabello (Neural Comput. Appl., 2023)

Enhancing Echocardiography Quality with Diffusion Neural Models. - Antonio Fernández-Rodríguez; Ezequiel López-Rubio; Pablo Torres-Salomón; Jorge Rodríguez-Capitán; Manuel Jiménez-Navarro; Miguel A. Molina-Cabello (IWBBIO (2), 2024)

Unsupervised Detection of Incoming and Outgoing Traffic Flows in Video Sequences. - José David Fernández-Rodríguez; Pablo Carmona-Martínez; Rafaela Benítez-Rochel; Miguel A. Molina-Cabello; Ezequiel López-Rubio (IWINAC (2), 2024)

Early Breast Cancer Detection by Automated Analysis of Mammograms with Deep Convolutional Networks. - Guillermo Tell-González; Ezequiel López-Rubio; Rafaela Benítez-Rochel; Miguel A. Molina-Cabello (SOCO (2), 2024)

The Impact of Noise and Brightness on Object Detection Methods. - José A. Rodríguez-Rodríguez; Ezequiel López-Rubio; Juan A. Ángel-Ruiz; Miguel A. Molina-Cabello (Sensors, 2024)

Enhancing Histopathological Image Classification Performance through Synthetic Data Generation with Generative Adversarial Networks. - Jose L. Ruiz-Casado; Miguel A. Molina-Cabello; Rafael M. Luque-Baena (Sensors, 2024)

CADICA: A new dataset for coronary artery disease detection by using invasive coronary angiography. - Ariadna Jiménez-Partinen; Miguel A. Molina-Cabello; Karl Thurnhofer-Hemsi; Esteban J. Palomo; Jorge Rodríguez-Capitán; Ana I. Molina-Ramos; Manuel Jiménez-Navarro (Expert Syst. J. Knowl. Eng., 2024)