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Enrique Domínguez
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2020 – today
- 2024
- [j31]Iván García Aguilar
, Jorge García-González
Detection of dangerously approaching vehicles over onboard cameras by speed estimation from apparent size. Neurocomputing 567: 127057 (2024) - [c62]Alvaro Rey-Blanes, Enrique Domínguez
Applying Deep Learning Based Super-Resolution to Knee Imaging. AIiH (2) 2024: 77-88 - [c61]Diego Teran-Pineda, Karl Thurnhofer-Hemsi, José David Fernández-Rodríguez
Deep Learning Models for Gait Event Prediction. IJCNN 2024: 1-8 - 2023
- [j30]Iván García Aguilar
Optimized instance segmentation by super-resolution and maximal clique generation. Integr. Comput. Aided Eng. 30(3): 243-256 (2023) - [j29]Diego Teran-Pineda
Human Gait Activity Recognition Using Multimodal Sensors. Int. J. Neural Syst. 33(11): 2350058:1-2350058:15 (2023) - [j28]José David Fernández-Rodríguez, Esteban J. Palomo
A convolutional autoencoder and a neural gas model based on Bregman divergences for hierarchical color quantization. Neurocomputing 544: 126288 (2023) - [j27]J. M. Górriz
Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends. Inf. Fusion 100: 101945 (2023) - [j26]Ricardo Javier Fuentes-Fino, Saúl Calderón Ramírez
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) - [j25]Iván García Aguilar
Small-Scale Urban Object Anomaly Detection Using Convolutional Neural Networks with Probability Estimation. Sensors 23(16): 7185 (2023) - [c60]Iván García Aguilar
Minimal Optimal Region Generation for Enhanced Object Detection in Aerial Images Using Super-Resolution and Convolutional Neural Networks. IWANN (1) 2023: 276-287 - [c59]Gonzalo Surribas Sayago
Photovoltaic Energy Prediction Using Machine Learning Techniques. IWANN (1) 2023: 577-587 - [c58]Jesús Benito-Picazo, Jose David Fernández Rodriguez
Parallel Processing Applied to Object Detection with a Jetson TX2 Embedded System. SOCO (2) 2023: 184-194 - 2022
- [c57]Nameer Mezher Baht, Enrique Domínguez:
Detection of Plant Diseases Based On Convolutional Neural Network Approach: a Review. ECAI 2022: 1-4 - [c56]Marcos Sergio Pacheco dos Santos Lima Junior
Enhanced Perspective Generation by Consensus of NeX neural models. IJCNN 2022: 1-8 - [c55]Ricardo Javier Fuentes-Fino, Saúl Calderón Ramírez
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 - [c54]Jorge García-González
Vehicle Overtaking Hazard Detection over Onboard Cameras Using Deep Convolutional Networks. SOCO 2022: 330-339 - 2021
- [j24]Karl Thurnhofer-Hemsi
Skin Lesion Classification by Ensembles of Deep Convolutional Networks and Regularly Spaced Shifting. IEEE Access 9: 112193-112205 (2021) - [j23]Karl Thurnhofer-Hemsi
A Convolutional Neural Network Framework for Accurate Skin Cancer Detection. Neural Process. Lett. 53(5): 3073-3093 (2021) - [c53]Miguel A. Molina-Cabello, Karl Thurnhofer-Hemsi
Longitudinal Study of the Learning Styles Evolution in Engineering Degrees. CISIS-ICEUTE 2021: 264-273 - [c52]Karl Thurnhofer-Hemsi
Peer Assessments in Engineering: A Pilot Project. CISIS-ICEUTE 2021: 274-283 - [c51]Jesús Benito-Picazo
Deep learning-based anomalous object detection system for panoramic cameras managed by a Jetson TX2 board. IJCNN 2021: 1-7 - [c50]Karl Thurnhofer-Hemsi
Enhanced transfer learning model by image shifting on a square lattice for skin lesion malignancy assessment. IJCNN 2021: 1-7 - [c49]Esteban J. Palomo
Hierarchical Color Quantization with a Neural Gas Model Based on Bregman Divergences. SOCO 2021: 327-337 - 2020
- [j22]Jesús Benito-Picazo
Deep learning-based video surveillance system managed by low cost hardware and panoramic cameras. Integr. Comput. Aided Eng. 27(4): 373-387 (2020) - [j21]Karl Thurnhofer-Hemsi
Deep learning-based super-resolution of 3D magnetic resonance images by regularly spaced shifting. Neurocomputing 398: 314-327 (2020) - [c48]P. Diego Teran, Enrique Domínguez:
Human gait model based on a machine learning and filtering noisy signals with recursive algorithm. BIBM 2020: 1142-1145 - [c47]Jesús Benito-Picazo
Image Clustering Using a Growing Neural Gas with Forbidden Regions. IJCNN 2020: 1-7
2010 – 2019
- 2019
- [j20]Jesús Benito-Picazo
Motion detection with low cost hardware for PTZ cameras. Integr. Comput. Aided Eng. 26(1): 21-36 (2019) - [j19]Ezequiel López-Rubio
Piecewise Polynomial Activation Functions for Feedforward Neural Networks. Neural Process. Lett. 50(1): 121-147 (2019) - [c46]Enrique Domínguez, Ezequiel López-Rubio, Miguel A. Molina-Cabello:
Cooperative Evaluation Using Moodle. CISIS-ICEUTE 2019: 295-301 - [c45]Can Cui, Karl Thurnhofer-Hemsi
Diabetic Wound Segmentation using Convolutional Neural Networks. EMBC 2019: 1002-1005 - [c44]Karl Thurnhofer-Hemsi
Analyzing Digital Image by Deep Learning for Melanoma Diagnosis. IWANN (2) 2019: 270-279 - [c43]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 - [c42]Jesús Benito-Picazo
Deep Learning-Based Security System Powered by Low Cost Hardware and Panoramic Cameras. IWINAC (2) 2019: 317-326 - 2018
- [j18]Francisco Javier López-Rubio, Ezequiel López-Rubio
The effect of noise on foreground detection algorithms. Artif. Intell. Rev. 49(3): 407-438 (2018) - [j17]Karl Thurnhofer-Hemsi
Panorama construction for PTZ camera surveillance with the neural gas network. Expert Syst. J. Knowl. Eng. 35(2) (2018) - [j16]Miguel A. Molina-Cabello, Ezequiel López-Rubio
Foreground object detection for video surveillance by fuzzy logic based estimation of pixel illumination states. Log. J. IGPL 26(6): 593-604 (2018) - [j15]Ezequiel López-Rubio
Foreground Detection by Competitive Learning for Varying Input Distributions. Int. J. Neural Syst. 28(5): 1750056:1-1750056:16 (2018) - [c41]Gongjin Lan
Real-Time Robot Vision on Low-Performance Computing Hardware. ICARCV 2018: 1959-1965 - [c40]Jesús Benito-Picazo, Enrique Domínguez, Esteban J. Palomo
Deep learning-based anomalous object detection system powered by microcontroller for PTZ cameras. IJCNN 2018: 1-7 - [c39]Karl Thurnhofer-Hemsi
Super-resolution of 3D Magnetic Resonance Images by Random Shifting and Convolutional Neural Networks. IJCNN 2018: 1-8 - [c38]Rafael Marcos Luque Baena
Foreground Detection Enhancement Using Pearson Correlation Filtering. IPMU (3) 2018: 417-428 - 2017
- [j14]Miguel A. Molina-Cabello, Rafael M. Luque-Baena, Ezequiel López-Rubio, Juan Miguel Ortiz-de-Lazcano-Lobato
A Growing Neural Gas Approach to Classify Vehicles in Traffic Environments. Int. J. Comput. Vis. Image Process. 7(3): 1-12 (2017) - [j13]Jesús Benito-Picazo
Growing Neural Forest-Based Color Quantization Applied to RGB Images. Int. J. Comput. Vis. Image Process. 7(3): 13-25 (2017) - [j12]Ezequiel López-Rubio
Dynamic tree topology learning by self-organization. Neural Comput. Appl. 28(5): 911-924 (2017) - [c37]Karl Thurnhofer-Hemsi
Panoramic background modeling for PTZ cameras with competitive learning neural networks. IJCNN 2017: 396-403 - [c36]Miguel A. Molina-Cabello, Ezequiel López-Rubio
Neural controller for PTZ cameras based on nonpanoramic foreground detection. IJCNN 2017: 404-411 - [c35]Miguel A. Molina-Cabello, Rafael Marcos Luque Baena
Vehicle Classification in Traffic Environments Using the Growing Neural Gas. IWANN (2) 2017: 225-234 - [c34]Esteban José Palomo
Unsupervised Color Quantization with the Growing Neural Forest. IWANN (2) 2017: 306-316 - [c33]Jesús Benito-Picazo
Motion Detection by Microcontroller for Panning Cameras. IWINAC (2) 2017: 279-288 - [c32]Enrique Domínguez, Ezequiel López-Rubio
Developing Cooperative Evaluation Methodologies in Higher Education. SOCO-CISIS-ICEUTE 2017: 706-711 - 2016
- [j11]Francisco Javier López-Rubio
Selecting the Color Space for Self-Organizing Map Based Foreground Detection in Video. Neural Process. Lett. 43(2): 345-361 (2016) - [c31]Miguel A. Molina-Cabello, Ezequiel López-Rubio
Frame Size Reduction for Foreground Detection in Video Sequences. CAEPIA 2016: 3-12 - [c30]Miguel A. Molina-Cabello, Ezequiel López-Rubio
Pixel Features for Self-organizing Map Based Detection of Foreground Objects in Dynamic Environments. SOCO-CISIS-ICEUTE 2016: 247-255 - 2015
- [j10]Ezequiel López-Rubio
Robust self-organization with M-estimators. Neurocomputing 151: 408-423 (2015) - [j9]Rafael Marcos Luque Baena
A self-organizing map to improve vehicle detection in flow monitoring systems. Soft Comput. 19(9): 2499-2509 (2015) - [c29]Ezequiel López-Rubio
Visualization of Complex Datasets with the Self-Organizing Spanning Tree. IWANN (1) 2015: 209-217 - 2014
- [j8]Ezequiel López-Rubio
Bregman Divergences for Growing Hierarchical Self-Organizing Networks. Int. J. Neural Syst. 24(4) (2014) - [j7]Esteban J. Palomo
Hierarchical Color Quantization Based on Self-organization. J. Math. Imaging Vis. 49(1): 1-19 (2014) - [c28]Francisco Javier López-Rubio
Color space selection for self-organizing map based foreground detection in video sequences. IJCNN 2014: 3347-3354 - 2013
- [j6]Rafael Marcos Luque Baena
A Competitive Neural Network for Multiple Object Tracking in Video Sequence Analysis. Neural Process. Lett. 37(1): 47-67 (2013) - [j5]Esteban J. Palomo
Image Compression and Video Segmentation Using Hierarchical Self-Organization. Neural Process. Lett. 37(1): 69-87 (2013) - [c27]Esteban José Palomo
Hierarchical Self-Organizing Networks for Multispectral Data Visualization. IWANN (2) 2013: 449-457 - [c26]Rafael Marcos Luque Baena
A Self-organizing Map for Traffic Flow Monitoring. IWANN (2) 2013: 458-466 - [c25]Jose Antonio Serra, José García Rodríguez, Sergio Orts-Escolano, Juan Manuel García Chamizo
3D Hand Pose Estimation with Neural Networks. IWANN (2) 2013: 504-512 - 2012
- [p2]Esteban J. Palomo
SOM-Based Techniques towards Hierarchical Visualisation of Network Forensics Traffic Data. Computational Intelligence for Privacy and Security 2012: 75-95 - 2011
- [j4]Ezequiel López-Rubio
Foreground Detection in Video Sequences with Probabilistic Self-Organizing Maps. Int. J. Neural Syst. 21(3): 225-246 (2011) - [c24]Gracián Triviño, José Muñoz, Enrique Domínguez:
A Multivalued Recurrent Neural Network for the Quadratic Assignment Problem. EANN/AIAI (2) 2011: 132-140 - [c23]Esteban J. Palomo
Image compression based on growing hierarchical Self-Organizing Maps. IJCNN 2011: 1624-1628 - [c22]Esteban J. Palomo
Lossy Image Compression Using a GHSOM. IWANN (2) 2011: 1-8 - [c21]Rafael Marcos Luque
Feature Weighting in Competitive Learning for Multiple Object Tracking in Video Sequences. IWANN (2) 2011: 17-24 - [c20]José García Rodríguez
Video and Image Processing with Self-Organizing Neural Networks. IWANN (2) 2011: 98-104 - 2010
- [c19]Rafael Marcos Luque, Enrique Domínguez, Esteban J. Palomo, José Muñoz:
An ART-type network approach for video object detection. ESANN 2010 - [c18]Esteban J. Palomo, Enrique Domínguez, Rafael Marcos Luque, José Muñoz:
Web Document Clustering based on a Hierarchical Self-Organizing Model. ESANN 2010 - [c17]Gracián Triviño, José Muñoz, Enrique Domínguez
A Recurrent Neural Network for Channel Assignment Problems in Mobiles. IBERAMIA 2010: 406-412 - [c16]Esteban J. Palomo
An anomaly detection system using a GHSOM-1. IJCNN 2010: 1-7
2000 – 2009
- 2009
- [c15]Esteban J. Palomo
A Self-Organized Multiagent System for Intrusion Detection. ADMI 2009: 84-94 - [c14]Esteban J. Palomo
Network Security Using Growing Hierarchical Self-Organizing Maps. ICANNGA 2009: 130-139 - [c13]Esteban J. Palomo
Spam Detection Based on a Hierarchical Self-Organizing Map. ICIC (2) 2009: 30-37 - [c12]Esteban J. Palomo
Image Hierarchical Segmentation Based on a GHSOM. ICONIP (1) 2009: 743-750 - [c11]Rafael Marcos Luque
A Neural Recognition System for Manufactured Objects. IWANN (2) 2009: 1274-1281 - 2008
- [j3]Enrique Domínguez
A neural model for the p-median problem. Comput. Oper. Res. 35(2): 404-416 (2008) - [c10]Esteban J. Palomo
An Intrusion Detection System Based on Hierarchical Self-Organization. CISIS 2008: 139-146 - [c9]Rafael Marcos Luque
A Dipolar Competitive Neural Network for Video Segmentation. IBERAMIA 2008: 103-112 - [c8]Esteban J. Palomo
A New GHSOM Model Applied to Network Security. ICANN (1) 2008: 680-689 - [c7]Rafael Marcos Luque Baena
A Neighborhood-Based Competitive Network for Video Segmentation and Object Detection. ICANN (1) 2008: 877-886 - [c6]Rafael Marcos Luque
A Neural Network Approach for Video Object Segmentation in Traffic Surveillance. ICIAR 2008: 151-158 - [c5]Esteban J. Palomo
A Competitive Neural Network for Intrusion Detection Systems. MCO 2008: 530-537 - [c4]Rafael Marcos Luque, Francisco L. Valverde, Enrique Domínguez, Esteban J. Palomo, José Muñoz:
Detecting Critical Situation in Public Transport. PRIS 2008: 57-66 - [p1]Enrique Domínguez, José Muñoz:
A Hybrid Algorithm for Solving Clustering Problems. Innovations in Hybrid Intelligent Systems 2008: 128-135 - 2007
- [c3]Enrique Domínguez, José Muñoz:
Solving Clustering Problems Using Hybrid Approaches. GEM 2007: 38-42 - 2006
- [j2]Enrique Alba
Comparative analysis of modern optimization tools for the p-median problem. Stat. Comput. 16(3): 251-260 (2006) - 2005
- [c2]Enrique Domínguez, José Muñoz:
Applying Bio-inspired Techniques to the p-Median Problem. IWANN 2005: 67-74 - 2004
- [j1]Enrique Domínguez
RealNet: a neural network architecture for real-time systems scheduling. Neural Comput. Appl. 13(4): 281-287 (2004) - 2003
- [c1]Enrique Domínguez, José Muñoz, Enrique Mérida Casermeiro:
A Recurrent Neural Network Model for the p-hub Problem. IWANN (2) 2003: 734-741
Coauthor Index
[j31] [j30] [j28] [j26] [j25] [c60] [c58] [c56] [c55] [c54] [j24] [c53] [c52] [c51] [c50] [c49] [j22] [j21] [j20] [j19] [c46] [c45] [c42] [j18] [j17] [j16] [j15] [c40] [c39] [c38] [j14] [j13] [j12] [c37] [c36] [c35] [c34] [c33] [c32] [j11] [c31] [c30] [j10] [j9] [c29] [j8] [c28] [j6] [c27] [c26] [j4] [c21]
Esteban J. Palomo
aka: Esteban José Palomo
aka: Esteban José Palomo
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last updated on 2024-10-07 22:08 CEST by the dblp team
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