Search Results for author: Siham Tabik

Found 13 papers, 5 papers with code

Shrub of a thousand faces: an individual segmentation from satellite images using deep learning

no code implementations31 Jan 2024 Rohaifa Khaldi, Siham Tabik, Sergio Puertas-Ruiz, Julio Peñas de Giles, José Antonio Hódar Correa, Regino Zamora, Domingo Alcaraz Segura

In this study, we propose a new data construction design that consists in using photo interpreted (PI) and field work (FW) data to respectively develop and externally validate the model.

Instance Segmentation Semantic Segmentation

Bidirectional recurrent imputation and abundance estimation of LULC classes with MODIS multispectral time series and geo-topographic and climatic data

1 code implementation11 Oct 2023 José Rodríguez-Ortega, Rohaifa Khaldi, Domingo Alcaraz-Segura, Siham Tabik

Experimental results demonstrate that integrating spectral-temporal input data with geo-topographic and climatic information significantly improves the estimation of LULC abundances in mixed pixels.

Imputation Time Series

CI-dataset and DetDSCI methodology for detecting too small and too large critical infrastructures in satellite images: Airports and electrical substations as case study

no code implementations25 May 2021 Francisco Pérez-Hernández, José Rodríguez-Ortega, Yassir Benhammou, Francisco Herrera, Siham Tabik

However, the detection of such infrastructures is complex as they have highly variable shapes and sizes, i. e., some infrastructures, such as electrical substations, are too small while others, such as airports, are too large.

Anomaly Detection Change Detection

MULTICAST: MULTI Confirmation-level Alarm SysTem based on CNN and LSTM to mitigate false alarms for handgun detection in video-surveillance

no code implementations23 Apr 2021 Roberto Olmos, Siham Tabik, Francisco Perez-Hernandez, Alberto Lamas, Francisco Herrera

Despite the constant advances in computer vision, integrating modern single-image detectors in real-time handgun alarm systems in video-surveillance is still debatable.

Lights and Shadows in Evolutionary Deep Learning: Taxonomy, Critical Methodological Analysis, Cases of Study, Learned Lessons, Recommendations and Challenges

no code implementations9 Aug 2020 Aritz D. Martinez, Javier Del Ser, Esther Villar-Rodriguez, Eneko Osaba, Javier Poyatos, Siham Tabik, Daniel Molina, Francisco Herrera

In summary, three axes - optimization and taxonomy, critical analysis, and challenges - which outline a complete vision of a merger of two technologies drawing up an exciting future for this area of fusion research.

FuCiTNet: Improving the generalization of deep learning networks by the fusion of learned class-inherent transformations

1 code implementation17 May 2020 Manuel Rey-Area, Emilio Guirado, Siham Tabik, Javier Ruiz-Hidalgo

It is widely known that very small datasets produce overfitting in Deep Neural Networks (DNNs), i. e., the network becomes highly biased to the data it has been trained on.

Data Augmentation General Classification +1

Deep Learning in Video Multi-Object Tracking: A Survey

no code implementations18 Jul 2019 Gioele Ciaparrone, Francisco Luque Sánchez, Siham Tabik, Luigi Troiano, Roberto Tagliaferri, Francisco Herrera

The problem of Multiple Object Tracking (MOT) consists in following the trajectory of different objects in a sequence, usually a video.

Multi-Object Tracking Multiple Object Tracking +1

Towards Highly Accurate Coral Texture Images Classification Using Deep Convolutional Neural Networks and Data Augmentation

no code implementations27 Mar 2018 Anabel Gómez-Ríos, Siham Tabik, Julián Luengo, ASM Shihavuddin, Bartosz Krawczyk, Francisco Herrera

The recognition of coral species based on underwater texture images pose a significant difficulty for machine learning algorithms, due to the three following challenges embedded in the nature of this data: 1) datasets do not include information about the global structure of the coral; 2) several species of coral have very similar characteristics; and 3) defining the spatial borders between classes is difficult as many corals tend to appear together in groups.

Data Augmentation General Classification +1

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

no code implementations3 Jun 2017 Emilio Guirado, Siham Tabik, Domingo Alcaraz-Segura, Javier Cabello, Francisco Herrera

There is a growing demand for accurate high-resolution land cover maps in many fields, e. g., in land-use planning and biodiversity conservation.

Data Augmentation Object Recognition +1

Automatic Handgun Detection Alarm in Videos Using Deep Learning

1 code implementation16 Feb 2017 Roberto Olmos, Siham Tabik, Francisco Herrera

Current surveillance and control systems still require human supervision and intervention.

Region Proposal

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