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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

017
Publications

2021

Remote Hyperspectral Imaging Acquisition and Characterization for Marine Litter Detection

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

Publication
Remote Sensing

Abstract
This paper addresses the development of a remote hyperspectral imaging system for detection and characterization of marine litter concentrations in an oceanic environment. The work performed in this paper is the following: (i) an in-situ characterization was conducted in an outdoor laboratory environment with the hyperspectral imaging system to obtain the spatial and spectral response of a batch of marine litter samples; (ii) a real dataset hyperspectral image acquisition was performed using manned and unmanned aerial platforms, of artificial targets composed of the material analyzed in the laboratory; (iii) comparison of the results (spatial and spectral response) obtained in laboratory conditions with the remote observation data acquired during the dataset flights; (iv) implementation of two different supervised machine learning methods, namely Random Forest (RF) and Support Vector Machines (SVM), for marine litter artificial target detection based on previous training. Obtained results show a marine litter automated detection capability with a 70–80% precision rate of detection in all three targets, compared to ground-truth pixels, as well as recall rates over 50%.

2021

Deep learning point cloud odometry: Existing approaches and open challenges

Authors
Teixeira, B; Silva, H;

Publication
U.Porto Journal of Engineering

Abstract
Achieving persistent and reliable autonomy for mobile robots in challenging field mission scenarios is a long-time quest for the Robotics research community. Deep learning-based LIDAR odometry is attracting increasing research interest as a technological solution for the robot navigation problem and showing great potential for the task. In this work, an examination of the benefits of leveraging learning-based encoding representations of real-world data is provided. In addition, a broad perspective of emergent Deep Learning robust techniques to track motion and estimate scene structure for real-world applications is the focus of a deeper analysis and comprehensive comparison. Furthermore, existing Deep Learning approaches and techniques for point cloud odometry tasks are explored, and the main technological solutions are compared and discussed. Open challenges are also laid out for the reader, hopefully offering guidance to future researchers in their quest to apply deep learning to complex 3D non-matrix data to tackle localization and robot navigation problems.

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

Supervised
thesis

2020

Deep Learning For Visual Odometry Estimation

Author
Bernardo Gomes Teixeira

Institution
UP-FEUP

2017

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

Author
Sara Freitas

Institution
UP-FEUP