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About

Hugo Miguel Silva was born in Porto, Portugal 1979. He finished is lic. degree in Electrical and Electronic Engineering from ISEP Porto Polytechnic School in 2004. He pursue further studies and obtained his Master in Electronics and Computers Engineering, from IST University of Lisbon in 2008.
In 2009 he obtained a PhD Scholarship from Portuguese Science Foundation (FCT), and graduated (Phd) in Electronics and Computers Engineering, from IST University of Lisbon in 2014.
He currently works in INESC TEC as a senior researcher, where he is project member in several international FP7, H2020 (SUNNY, VAMOS) projects.
He is the main author of several research publications in the domains of computer vision and mobile robotics applications.

Interest
Topics
Details

Details

012
Publications

2020

Deep Learning for Underwater Visual Odometry Estimation

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

Publication
IEEE Access

Abstract

2019

Convolutional neural network target detection in hyperspectral imaging for maritime surveillance

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

Publication
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

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

Publication
OCEANS 2019 MTS/IEEE SEATTLE

Abstract

2018

Supervised classification for hyperspectral imaging in UAV maritime target detection

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

Publication
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

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

Publication
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)

Supervised
thesis

2017

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

Author
Sara Freitas

Institution
UP-FEUP