Francisco Herrera

发表

Automatic handgun detection alarm in videos using deep learning

Francisco Herrera, Siham Tabik, Roberto Olmos, 2017, Neurocomputing.

Whale counting in satellite and aerial images with deep learning

Francisco Herrera, Siham Tabik, Domingo Alcaraz-Segura, 2019, Scientific Reports.

MonuMAI: Dataset, deep learning pipeline and citizen science based app for monumental heritage taxonomy and classification

Francisco Herrera, Siham Tabik, Rosana Montes-Soldado, 2021, Neurocomputing.

A robust approach for deep neural networks in presence of label noise: relabelling and filtering instances during training

J. Luengo, Francisco Herrera, A. G'omez-R'ios, 2021, ArXiv.

Sentinel2GlobalLULC: A Sentinel-2 RGB image tile dataset for global land use/cover mapping with deep learning

S. Tabik, Francisco Herrera, D. Alcaraz‐Segura, 2022, Scientific Data.

EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: the MonuMAI cultural heritage use case pdf

Ivan Donadello, Siham Tabik, Gianni Franchi, 2021, Inf. Fusion.

A tutorial on the segmentation of metallographic images: Taxonomy, new MetalDAM dataset, deep learning-based ensemble model, experimental analysis and challenges

Pablo Mesejo, Julián Luengo, David Charte, 2021, Inf. Fusion.

Object Detection Binary Classifiers methodology based on deep learning to identify small objects handled similarly: Application in video surveillance

Francisco Herrera, Hamido Fujita, Siham Tabik, 2020, Knowl. Based Syst..

Detection of Fir Trees (Abies sibirica) Damaged by the Bark Beetle in Unmanned Aerial Vehicle Images with Deep Learning

Francisco Herrera, Siham Tabik, Domingo Alcaraz-Segura, 2019, Remote. Sens..

Deep Learning in Video Multi-Object Tracking: A Survey

Francisco Herrera, Luigi Troiano, Roberto Tagliaferri, 2019, Neurocomputing.

A tutorial on ensembles and deep learning fusion with MNIST as guiding thread: A complex heterogeneous fusion scheme reaching 10 digits error

M. M. Ruiz, A. R. Figueiras-Vidal, S. Tabik, 2020, ArXiv.

Human pose estimation for mitigating false negatives in weapon detection in video-surveillance

Antonio Cano Montes, S. Tabik, R. Olmos, 2022, Neurocomputing.

Deep-learning Versus OBIA for Scattered Shrub Detection with Google Earth Imagery: Ziziphus lotus as Case Study

Francisco Herrera, Siham Tabik, Domingo Alcaraz-Segura, 2017, Remote. Sens..

TimeSpec4LULC: a global multispectral time series database for training LULC mapping models with machine learning

S. Tabik, Francisco Herrera, D. Alcaraz‐Segura, 2022, Earth System Science Data.

TimeSpec4LULC: A Global Deep Learning-driven Dataset of MODIS Terra-Aqua Multi-Spectral Time Series for LULC Mapping and Change Detection

S. Tabik, Francisco Herrera, D. Alcaraz‐Segura, 2021 .

Sentinel2GlobalLULC: A deep-learning-ready Sentinel-2 RGB image dataset for global land use/cover mapping

S. Tabik, Francisco Herrera, E. Guirado, 2021, bioRxiv.

Brightness guided preprocessing for automatic cold steel weapon detection in surveillance videos with deep learning

Francisco Herrera, Alberto Castillo, Siham Tabik, 2019, Neurocomputing.

REVEL Framework to measure Local Linear Explanations for black-box models: Deep Learning Image Classification case of study

Francisco Herrera, I. Sevillano-Garc'ia, Juli'an Luengo-Mart'in, 2022, International Journal of Intelligent Systems.

Deep-Learning Convolutional Neural Networks for scattered shrub detection with Google Earth Imagery pdf

Francisco Herrera, Siham Tabik, Domingo Alcaraz-Segura, 2017, ArXiv.

Automatic whale counting in satellite images with deep learning

Francisco Herrera, Siham Tabik, Emilio Guirado, 2018, bioRxiv.

Tree Cover Estimation in Global Drylands from Space Using Deep Learning

Francisco Herrera, Siham Tabik, Javier Cabello, 2020, Remote. Sens..

Deep learning in video multi-object tracking: A survey

Francisco Luque Sánchez, R. Tagliaferri, S. Tabik, 2019, Neurocomputing.

Deep Learning in Video Multi-Object Tracking: A Survey

Francisco Luque Sánchez, S. Tabik, Francisco Herrera, 2019, arXiv.org.

MNIST-NET10: A heterogeneous deep networks fusion based on the degree of certainty to reach 0.1% error rate. Ensembles overview and proposal

R. F. Alvear-Sandoval, A. Figueiras-Vidal, S. Tabik, 2020, Inf. Fusion.

CI-Dataset and DetDSCI Methodology for Detecting Too Small and Too Large Critical Infrastructures in Satellite Images: Airports and Electrical Substations as Case Study

Siham Tabik, Yassir Benhammou, Francisco Pérez-Hernández, 2021, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

Small and large scale critical infrastructures detection based on deep learning using high resolution orthogonal images pdf

Siham Tabik, Yassir Benhammou, Francisco Pérez-Hernández, 2021, ArXiv.