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ORCIDMiguel A. Molina-Cabello
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2020 – today
- 2024
[j26]Ariadna Jiménez-Partinen
, Miguel A. Molina-Cabello, Karl Thurnhofer-Hemsi, Esteban J. Palomo, Jorge Rodríguez-Capitán, Ana I. Molina-Ramos, Manuel Jiménez-Navarro:
CADICA: A new dataset for coronary artery disease detection by using invasive coronary angiography. Expert Syst. J. Knowl. Eng. 41(12) (2024)- [j25]José A. Rodríguez-Rodríguez, Ezequiel López-Rubio, Juan A. Ángel-Ruiz, Miguel A. Molina-Cabello:
The Impact of Noise and Brightness on Object Detection Methods. Sensors 24(3): 821 (2024) - [j24]Jose L. Ruiz-Casado, Miguel A. Molina-Cabello, Rafael M. Luque-Baena:
Enhancing Histopathological Image Classification Performance through Synthetic Data Generation with Generative Adversarial Networks. Sensors 24(12): 3777 (2024) - [c38]Antonio Fernández-Rodríguez, Ezequiel López-Rubio, Pablo Torres-Salomón, Jorge Rodríguez-Capitán, Manuel Jiménez-Navarro, Miguel A. Molina-Cabello:
Enhancing Echocardiography Quality with Diffusion Neural Models. IWBBIO (2) 2024: 169-181 - [c37]José David Fernández-Rodríguez, Pablo Carmona-Martínez, Rafaela Benítez-Rochel, Miguel A. Molina-Cabello, Ezequiel López-Rubio:
Unsupervised Detection of Incoming and Outgoing Traffic Flows in Video Sequences. IWINAC (2) 2024: 3-12 - [i6]Ariadna Jiménez-Partinen, Miguel A. Molina-Cabello, Karl Thurnhofer-Hemsi, Esteban J. Palomo, Jorge Rodríguez-Capitán, Ana I. Molina-Ramos, Manuel Jiménez-Navarro:
CADICA: a new dataset for coronary artery disease detection by using invasive coronary angiography. CoRR abs/2402.00570 (2024) - 2023
- [j23]Miguel A. Molina-Cabello, Karl Thurnhofer-Hemsi, David Molina-Cabello, Esteban J. Palomo:
Are learning styles useful? A new software to analyze correlations with grades and a case study in engineering. Comput. Appl. Eng. Educ. 31(3): 537-551 (2023) - [j22]Jose David Fernández Rodriguez, Jorge García-González, Rafaela Benítez-Rochel, Miguel A. Molina-Cabello, Gonzalo Ramos-Jiménez, Ezequiel López-Rubio:
Automated detection of vehicles with anomalous trajectories in traffic surveillance videos. Integr. Comput. Aided Eng. 30(3): 293-309 (2023) - [j21]Ricardo Javier Fuentes-Fino, Saúl Calderón Ramírez, Enrique Domínguez, Ezequiel López-Rubio, David A. Elizondo, Miguel A. Molina-Cabello:
An uncertainty estimator method based on the application of feature density to classify mammograms for breast cancer detection. Neural Comput. Appl. 35(30): 22151-22161 (2023) - [j20]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:
Dataset Similarity to Assess Semisupervised Learning Under Distribution Mismatch Between the Labeled and Unlabeled Datasets. IEEE Trans. Artif. Intell. 4(2): 282-291 (2023) - 2022
- [j19]Saúl Calderón Ramírez, Diego Murillo-Hernandez, Kevin Rojas-Salazar, David A. Elizondo, Shengxiang Yang, Armaghan Moemeni, Miguel A. Molina-Cabello:
A real use case of semi-supervised learning for mammogram classification in a local clinic of Costa Rica. Medical Biol. Eng. Comput. 60(4): 1159-1175 (2022) - [c36]Ricardo Javier Fuentes-Fino, Saúl Calderón Ramírez, Enrique Domínguez, Ezequiel López-Rubio, Marco A. Hernandez-Vasquez, Miguel A. Molina-Cabello:
Feature Density as an Uncertainty Estimator Method in the Binary Classification Mammography Images Task for a Supervised Deep Learning Model. IWBBIO (2) 2022: 375-388 - [c35]José M. Pérez-Bravo, José A. Rodríguez-Rodríguez, Jorge García-González, Miguel A. Molina-Cabello, Karl Thurnhofer-Hemsi, Ezequiel López-Rubio:
Encoding Generative Adversarial Networks for Defense Against Image Classification Attacks. IWINAC (1) 2022: 163-172 - [c34]Jose D. Fernández, Jorge García-González, Rafaela Benítez-Rochel, Miguel A. Molina-Cabello, Ezequiel López-Rubio:
Anomalous Trajectory Detection for Automated Traffic Video Surveillance. IWINAC (1) 2022: 173-182 - 2021
- [j18]Noel Khan, David A. Elizondo, Lipika Deka, Miguel A. Molina-Cabello:
Fuzzy Logic Applied to System Monitors. IEEE Access 9: 56523-56538 (2021) - [j17]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:
Improving Uncertainty Estimation With Semi-Supervised Deep Learning for COVID-19 Detection Using Chest X-Ray Images. IEEE Access 9: 85442-85454 (2021) - [j16]Saúl Calderón Ramírez, Shengxiang Yang, Armaghan Moemeni, David A. Elizondo, Simon Colreavy-Donnelly, Luis Fernando Chavarria-Estrada, Miguel A. Molina-Cabello:
Correcting data imbalance for semi-supervised COVID-19 detection using X-ray chest images. Appl. Soft Comput. 111: 107692 (2021) - [j15]Jorge García-González, Miguel A. Molina-Cabello, Rafael M. Luque-Baena, Juan Miguel Ortiz-de-Lazcano-Lobato, Ezequiel López-Rubio:
Road pollution estimation from vehicle tracking in surveillance videos by deep convolutional neural networks. Appl. Soft Comput. 113(Part): 107950 (2021) - [j14]Ezequiel López-Rubio, Miguel A. Molina-Cabello, Francisco M. Castro, Rafael M. Luque-Baena, Manuel J. Marín-Jiménez, Nicolás Guil:
Anomalous object detection by active search with PTZ cameras. Expert Syst. Appl. 181: 115150 (2021) - [c33]Miguel A. Molina-Cabello, Karl Thurnhofer-Hemsi, Enrique Domínguez, Ezequiel López-Rubio, Esteban J. Palomo:
Longitudinal Study of the Learning Styles Evolution in Engineering Degrees. CISIS-ICEUTE 2021: 264-273 - [c32]Karl Thurnhofer-Hemsi, Miguel A. Molina-Cabello, Esteban J. Palomo, Ezequiel López-Rubio, Enrique Domínguez:
Peer Assessments in Engineering: A Pilot Project. CISIS-ICEUTE 2021: 274-283 - [c31]Safa Hamreras, Bachir Boucheham, Miguel A. Molina-Cabello, Rafaela Benítez-Rochel, Ezequiel López-Rubio:
Dynamic selection of classifiers for Content Based Image Retrieval. IJCNN 2021: 1-8 - [c30]Miguel A. Molina-Cabello, José A. Rodríguez-Rodríguez, Karl Thurnhofer-Hemsi, Ezequiel López-Rubio:
Histopathological image analysis for breast cancer diagnosis by ensembles of convolutional neural networks and genetic algorithms. IJCNN 2021: 1-8 - [c29]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:
Improving Uncertainty Estimations for Mammogram Classification using Semi-Supervised Learning. IJCNN 2021: 1-8 - [c28]José A. Rodríguez-Rodríguez, Miguel A. Molina-Cabello, Rafaela Benítez-Rochel, Ezequiel López-Rubio:
Test time augmentation by regular shifting for deep denoising autoencoder networks. IJCNN 2021: 1-7 - [c27]Karl Thurnhofer-Hemsi, Rosa Maza-Quiroga, Enrique Domínguez, Miguel A. Molina-Cabello, Ezequiel López-Rubio:
Enhanced transfer learning model by image shifting on a square lattice for skin lesion malignancy assessment. IJCNN 2021: 1-7 - [c26]Willard Zamora-Cárdenas, Mauro Mendez, Saúl Calderón Ramírez, Martin Vargas, Gerardo Monge, Steve Quirós, David A. Elizondo, Jordina Torrents-Barrena, Miguel A. Molina-Cabello:
Enforcing Morphological Information in Fully Convolutional Networks to Improve Cell Instance Segmentation in Fluorescence Microscopy Images. IWANN (1) 2021: 36-46 - [c25]José Miguel López-Rubio, Miguel A. Molina-Cabello, Gonzalo Ramos-Jiménez, Ezequiel López-Rubio:
Classification of Images as Photographs or Paintings by Using Convolutional Neural Networks. IWANN (1) 2021: 432-442 - [i5]Willard Zamora-Cárdenas, Mauro Mendez, Saúl Calderón Ramírez, Martin Vargas, Gerardo Monge, Steve Quirós, David A. Elizondo, Miguel A. Molina-Cabello:
Enforcing Morphological Information in Fully Convolutional Networks to Improve Cell Instance Segmentation in Fluorescence Microscopy Images. CoRR abs/2106.05843 (2021) - [i4]Saúl Calderón Ramírez, Diego Murillo-Hernandez, Kevin Rojas-Salazar, David A. Elizondo, Shengxiang Yang, Miguel A. Molina-Cabello:
A Real Use Case of Semi-Supervised Learning for Mammogram Classification in a Local Clinic of Costa Rica. CoRR abs/2107.11696 (2021) - 2020
- [j13]Miguel A. Molina-Cabello, David A. Elizondo, Rafael Marcos Luque Baena, Ezequiel López-Rubio:
Aggregation of Convolutional Neural Network Estimations of Homographies by Color Transformations of the Inputs. IEEE Access 8: 79552-79560 (2020) - [j12]Miguel A. Molina-Cabello, Jorge García-González, Rafael M. Luque-Baena, Ezequiel López-Rubio:
The effect of downsampling-upsampling strategy on foreground detection algorithms. Artif. Intell. Rev. 53(7): 4935-4965 (2020) - [j11]Miguel A. Molina-Cabello, David A. Elizondo, Rafael M. Luque-Baena, Ezequiel López-Rubio:
Foreground detection by ensembles of random polygonal tilings. Expert Syst. Appl. 161: 113518 (2020) - [j10]Karl Thurnhofer-Hemsi, Ezequiel López-Rubio, Núria Roé-Vellvé, Miguel A. Molina-Cabello:
Multiobjective optimization of deep neural networks with combinations of Lp-norm cost functions for 3D medical image super-resolution. Integr. Comput. Aided Eng. 27(3): 233-251 (2020) - [j9]Safa Hamreras, Bachir Boucheham, Miguel A. Molina-Cabello, Rafaela Benítez-Rochel, Ezequiel López-Rubio:
Content based image retrieval by ensembles of deep learning object classifiers. Integr. Comput. Aided Eng. 27(3): 317-331 (2020) - [c24]Jorge García-González, Miguel A. Molina-Cabello, Rafael M. Luque-Baena, Juan Miguel Ortiz-de-Lazcano-Lobato, Ezequiel López-Rubio:
Deep Autoencoder Architectures For Foreground Object Detection In Video Sequences Based On Probabilistic Mixture Models. ICIP 2020: 3199-3203 - [c23]José A. Rodríguez-Rodríguez, Miguel A. Molina-Cabello, Rafaela Benítez-Rochel, Ezequiel López-Rubio:
The Impact of Linear Motion Blur on the Object Recognition Efficiency of Deep Convolutional Neural Networks. ICPR Workshops (6) 2020: 611-622 - [c22]Karl Thurnhofer-Hemsi, Guillermo Ruiz-Álvarez, Rafael Marcos Luque Baena, Miguel A. Molina-Cabello, Ezequiel López-Rubio:
Performance of Deep Learning and Traditional Techniques in Single Image Super-Resolution of Noisy Images. ICPR Workshops (6) 2020: 623-638 - [c21]José A. Rodríguez-Rodríguez, Miguel A. Molina-Cabello, Rafaela Benítez-Rochel, Ezequiel López-Rubio:
The Effect of Noise and Brightness on Convolutional Deep Neural Networks. ICPR Workshops (6) 2020: 639-654 - [c20]José A. Rodríguez-Rodríguez, Miguel A. Molina-Cabello, Rafaela Benítez-Rochel, Ezequiel López-Rubio:
The effect of image enhancement algorithms on convolutional neural networks. ICPR 2020: 3084-3089 - [c19]Miguel A. Molina-Cabello, Jorge García-González, Rafael Marcos Luque Baena, Karl Thurnhofer-Hemsi, Ezequiel López-Rubio:
Adaptive estimation of optimal color transformations for deep convolutional network based homography estimation. ICPR 2020: 3106-3113 - [c18]Saúl Calderón Ramírez, Raghvendra Giri, Shengxiang Yang, Armaghan Moemeni, Mario Umaña, David A. Elizondo, Jordina Torrents-Barrena, Miguel A. Molina-Cabello:
Dealing with Scarce Labelled Data: Semi-supervised Deep Learning with Mix Match for Covid-19 Detection Using Chest X-ray Images. ICPR 2020: 5294-5301 - [i3]Saúl Calderón Ramírez, Luis Oala, Jordina Torrents-Barrena, Shengxiang Yang, Armaghan Moemeni, Wojciech Samek, Miguel A. Molina-Cabello:
MixMOOD: A systematic approach to class distribution mismatch in semi-supervised learning using deep dataset dissimilarity measures. CoRR abs/2006.07767 (2020) - [i2]Karl Thurnhofer-Hemsi, Ezequiel López-Rubio, Miguel A. Molina-Cabello, Kayvan Najarian:
Radial basis function kernel optimization for Support Vector Machine classifiers. CoRR abs/2007.08233 (2020) - [i1]Saúl Calderón Ramírez, Shengxiang Yang, Armaghan Moemeni, David A. Elizondo, Simon Colreavy-Donnelly, Luis Fernando Chavarria-Estrada, Miguel A. Molina-Cabello:
Correcting Data Imbalance for Semi-Supervised Covid-19 Detection Using X-ray Chest Images. CoRR abs/2008.08496 (2020)
2010 – 2019
- 2019
- [j8]Jorge García-González, Juan Miguel Ortiz-de-Lazcano-Lobato, Rafael M. Luque-Baena, Miguel A. Molina-Cabello, Ezequiel López-Rubio:
Foreground detection by probabilistic modeling of the features discovered by stacked denoising autoencoders in noisy video sequences. Pattern Recognit. Lett. 125: 481-487 (2019) - [c17]Enrique Domínguez, Ezequiel López-Rubio, Miguel A. Molina-Cabello:
Cooperative Evaluation Using Moodle. CISIS-ICEUTE 2019: 295-301 - [c16]Miguel A. Molina-Cabello, Cristian Accino, Ezequiel López-Rubio, Karl Thurnhofer-Hemsi:
Optimization of Convolutional Neural Network Ensemble Classifiers by Genetic Algorithms. IWANN (2) 2019: 163-173 - [c15]Miguel A. Molina-Cabello, Benjamin N. Passow, Enrique Domínguez, David A. Elizondo, Jolanta Obszynska:
Infering Air Quality from Traffic Data Using Transferable Neural Network Models. IWANN (1) 2019: 832-843 - [c14]Safa Hamreras, Rafaela Benítez-Rochel, Bachir Boucheham, Miguel A. Molina-Cabello, Ezequiel López-Rubio:
Content Based Image Retrieval by Convolutional Neural Networks. IWINAC (2) 2019: 277-286 - [c13]Karl Thurnhofer-Hemsi, Ezequiel López-Rubio, Núria Roé-Vellvé, Miguel A. Molina-Cabello:
Deep Learning Networks with p-norm Loss Layers for Spatial Resolution Enhancement of 3D Medical Images. IWINAC (2) 2019: 287-296 - 2018
- [j7]Francisco Javier López-Rubio, Ezequiel López-Rubio, Miguel A. Molina-Cabello, Rafael Marcos Luque Baena, Esteban J. Palomo, Enrique Domínguez:
The effect of noise on foreground detection algorithms. Artif. Intell. Rev. 49(3): 407-438 (2018) - [j6]Karl Thurnhofer-Hemsi, Ezequiel López-Rubio, Enrique Domínguez, Rafael Marcos Luque Baena, Miguel A. Molina-Cabello:
Panorama construction for PTZ camera surveillance with the neural gas network. Expert Syst. J. Knowl. Eng. 35(2) (2018) - [j5]Miguel A. Molina-Cabello, Rafael Marcos Luque Baena, Ezequiel López-Rubio, Karl Thurnhofer-Hemsi:
Vehicle type detection by ensembles of convolutional neural networks operating on super resolved images. Integr. Comput. Aided Eng. 25(4): 321-333 (2018) - [j4]Miguel A. Molina-Cabello, Ezequiel López-Rubio, Rafael M. Luque-Baena, Enrique Domínguez, Esteban J. Palomo:
Foreground object detection for video surveillance by fuzzy logic based estimation of pixel illumination states. Log. J. IGPL 26(6): 593-604 (2018) - [j3]Ezequiel López-Rubio, Miguel A. Molina-Cabello, Rafael Marcos Luque Baena, Enrique Domínguez:
Foreground Detection by Competitive Learning for Varying Input Distributions. Int. J. Neural Syst. 28(5): 1750056:1-1750056:16 (2018) - [c12]Jorge García-González, Juan Miguel Ortiz-de-Lazcano-Lobato, Rafael M. Luque-Baena, Miguel A. Molina-Cabello, Ezequiel López-Rubio:
Background Modeling for Video Sequences by Stacked Denoising Autoencoders. CAEPIA 2018: 341-350 - [c11]Miguel A. Molina-Cabello, Rafael Marcos Luque Baena, Ezequiel López-Rubio, Lipika Deka, Karl Thurnhofer-Hemsi:
Road Pollution Estimation Using Static Cameras And Neural Networks. IJCNN 2018: 1-7 - [c10]Esteban J. Palomo, Miguel A. Molina-Cabello, Ezequiel López-Rubio, Rafael Marcos Luque Baena:
A New Self-Organizing Neural Gas Model based on Bregman Divergences. IJCNN 2018: 1-8 - [c9]Karl Thurnhofer-Hemsi, Ezequiel López-Rubio, Núria Roé-Vellvé, Enrique Domínguez, Miguel A. Molina-Cabello:
Super-resolution of 3D Magnetic Resonance Images by Random Shifting and Convolutional Neural Networks. IJCNN 2018: 1-8 - [c8]Rafael Marcos Luque Baena, Miguel A. Molina-Cabello, Ezequiel López-Rubio, Enrique Domínguez:
Foreground Detection Enhancement Using Pearson Correlation Filtering. IPMU (3) 2018: 417-428 - [c7]Miguel A. Molina-Cabello, Ezequiel López-Rubio, Rafael M. Luque-Baena, María Jesús Rodríguez-Espinosa, Karl Thurnhofer-Hemsi:
Blood Cell Classification Using the Hough Transform and Convolutional Neural Networks. WorldCIST (2) 2018: 669-678 - 2017
- [j2]Miguel A. Molina-Cabello, Rafael M. Luque-Baena, Ezequiel López-Rubio, Juan Miguel Ortiz-de-Lazcano-Lobato, Enrique Domínguez:
A Growing Neural Gas Approach to Classify Vehicles in Traffic Environments. Int. J. Comput. Vis. Image Process. 7(3): 1-12 (2017) - [c6]Karl Thurnhofer-Hemsi, Ezequiel López-Rubio, Enrique Domínguez, Rafael Marcos Luque Baena, Miguel A. Molina-Cabello:
Panoramic background modeling for PTZ cameras with competitive learning neural networks. IJCNN 2017: 396-403 - [c5]Miguel A. Molina-Cabello, Ezequiel López-Rubio, Rafael Marcos Luque Baena, Enrique Domínguez, Karl Thurnhofer-Hemsi:
Neural controller for PTZ cameras based on nonpanoramic foreground detection. IJCNN 2017: 404-411 - [c4]Miguel A. Molina-Cabello, Rafael Marcos Luque Baena, Ezequiel López-Rubio, Juan Miguel Ortiz-de-Lazcano-Lobato, Enrique Domínguez, José Muñoz-Pérez:
Vehicle Classification in Traffic Environments Using the Growing Neural Gas. IWANN (2) 2017: 225-234 - [c3]Miguel A. Molina-Cabello, Rafael Marcos Luque Baena, Ezequiel López-Rubio, Karl Thurnhofer-Hemsi:
Vehicle Type Detection by Convolutional Neural Networks. IWINAC (2) 2017: 268-278 - 2016
- [j1]Francisco Ortega-Zamorano, Miguel A. Molina-Cabello, Ezequiel López-Rubio, Esteban J. Palomo:
Smart motion detection sensor based on video processing using self-organizing maps. Expert Syst. Appl. 64: 476-489 (2016) - [c2]Miguel A. Molina-Cabello, Ezequiel López-Rubio, Rafael Marcos Luque Baena, Esteban J. Palomo, Enrique Domínguez:
Frame Size Reduction for Foreground Detection in Video Sequences. CAEPIA 2016: 3-12 - [c1]Miguel A. Molina-Cabello, Ezequiel López-Rubio, Rafael Marcos Luque Baena, Enrique Domínguez, Esteban J. Palomo:
Pixel Features for Self-organizing Map Based Detection of Foreground Objects in Dynamic Environments. SOCO-CISIS-ICEUTE 2016: 247-255
Coauthor Index
Rafael Marcos Luque Baena
aka: Rafael M. Luque-Baena
aka: Rafael M. Luque-Baena
[j25] [c38] [c37] [j22] [j21] [j20] [c36] [c35] [c34] [j17] [j15] [j14] [c33] [c32] [c31] [c30] [c29] [c28] [c27] [c25] [j13] [j12] [j11] [j10] [j9] [c24] [c23] [c22] [c21] [c20] [c19] [i2] [j8] [c17] [c16] [c14] [c13] [j7] [j6] [j5] [j4] [j3] [c12] [c11] [c10] [c9] [c8] [c7] [j2] [c6] [c5] [c4] [c3] [j1] [c2] [c1]
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