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Sobre
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Sobre

Nasci no Porto em 1979.  Completei os meus estudos de base (Licenciatura) em Engenharia Electrotecnica e de Computadores no ISEP em 2004. Em 2014 completei o Doutoramento em Electronica e Computadores no Instituto Superior Técnico. Actualmente exerco as funções de investigador sénior no INESC TEC, onde trabalho na área da robótica móvel em projectos de âmbito nacional e internacional.  Sou autor e revisor de várias publicações com arbitragem cientifica no dominio da visão por computador para aplicações de robótica móvel.

Tópicos
de interesse
Detalhes

Detalhes

016
Publicações

2020

Deep Learning for Underwater Visual Odometry Estimation

Autores
Teixeira, B; Silva, H; Matos, A; Silva, E;

Publicação
IEEE Access

Abstract

2019

Convolutional neural network target detection in hyperspectral imaging for maritime surveillance

Autores
Freitas, S; Silva, H; Almeida, JM; Silva, E;

Publicação
International Journal of Advanced Robotic Systems

Abstract
This work addresses a hyperspectral imaging system for maritime surveillance using unmanned aerial vehicles. The objective was to detect the presence of vessels using purely spatial and spectral hyperspectral information. To accomplish this objective, we implemented a novel 3-D convolutional neural network approach and compared against two implementations of other state-of-the-art methods: spectral angle mapper and hyperspectral derivative anomaly detection. The hyperspectral imaging system was developed during the SUNNY project, and the methods were tested using data collected during the project final demonstration, in São Jacinto Air Force Base, Aveiro (Portugal). The obtained results show that a 3-D CNN is able to improve the recall value, depending on the class, by an interval between 27% minimum, to a maximum of over 40%, when compared to spectral angle mapper and hyperspectral derivative anomaly detection approaches. Proving that 3-D CNN deep learning techniques that combine spectral and spatial information can be used to improve the detection of targets classification accuracy in hyperspectral imaging unmanned aerial vehicles maritime surveillance applications. © The Author(s) 2019.

2019

Deep Learning Approaches Assessment for Underwater Scene Understanding and Egomotion Estimation

Autores
Teixeira, B; Silva, H; Matos, A; Silva, E;

Publicação
OCEANS 2019 MTS/IEEE SEATTLE

Abstract

2018

Supervised classification for hyperspectral imaging in UAV maritime target detection

Autores
Freitas, S; Almeida, C; Silva, H; Almeida, J; Silva, E;

Publicação
18th IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2018

Abstract
This paper addresses the use of a hyperspectral image system to detect vessels in maritime operational scenarios. The developed hyperspectral imaging classification methods are based on supervised approaches and allow to detect the presence of vessels using real hyperspectral data. We implemented two different methods for comparison purposes: SVM and SAM. The SVM method, which can be considered one of most utilized methods for image classification, was implemented using linear, RBF, sigmoid and polynomial kernels with PCA for dimensionality reduction, and compared with SAM using a two classes definition, namely vessel and water. The obtained results using real data collected from a UAV allow to conclude that the SVM approach is suitable for detecting the vessel presence in the water with a precision and recall rates favorable when compared to SAM. © 2018 IEEE.

2018

Hyperspectral Imaging for Real-Time Unmanned Aerial Vehicle Maritime Target Detection

Autores
Freitas, S; Silva, H; Almeida, J; Silva, E;

Publicação
Journal of Intelligent and Robotic Systems: Theory and Applications

Abstract
This work address hyperspectral imaging systems use for maritime target detection using unmanned aerial vehicles. Specifically, by working in the creation of a hyperspectral real-time data processing system pipeline. We develop a boresight calibration method that allows to calibrate the position of the navigation sensor related to the camera imaging sensor, and improve substantially the accuracy of the target geo-reference. We also develop an unsupervised method for segmenting targets (boats) from their dominant background in real-time. We evaluated the performance of our proposed system for target detection in real-time with UAV flight data and present detection results comparing favorably our approach against other state-of- the-art method. © 2017 The Author(s)

Teses
supervisionadas

2017

Deep Learning Unmanned Robotic Hyperspectral Imaging for Sustainable Forests and Water Management

Autor
Sara Freitas

Instituição
UP-FEUP