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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.

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Details

Details

002
Publications

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)

2017

A voting method for stereo egomotion estimation

Authors
Silva, H; Bernardino, A; Silva, E;

Publication
International Journal of Advanced Robotic Systems

Abstract
The development of vision-based navigation systems for mobile robotics applications in outdoor scenarios is a very challenging problem due to frequent changes in contrast and illumination, image blur, pixel noise, lack of image texture, low image overlap and other effects that lead to ambiguity in the interpretation of motion from image data. To mitigate the problems arising from multiple possible interpretations of the data in outdoor stereo egomotion, we present a fully probabilistic method denoted as probabilistic stereo egomotion transform. Our method is capable of computing 6-degree of freedom motion parameters solely based on probabilistic correspondences without the need to track or commit key point matches between two consecutive frames. The use of probabilistic correspondence methods allows to maintain several match hypothesis for each point, which is an advantage when ambiguous matches occur (which is the rule in image feature correspondence problems), because no commitment is made before analysing all image information. Experimental validation is performed in simulated and real outdoor scenarios in the presence of image noise and image blur. Comparison with other current state-of-the-art visual motion estimation method is also provided. Our method is capable of significant reduction of estimation errors mainly in harsh conditions of noise and blur. © 2017, © The Author(s) 2017.

2015

Probabilistic Egomotion for Stereo Visual Odometry

Authors
Silva, H; Bernardino, A; Silva, E;

Publication
JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS

Abstract
We present a novel approach of Stereo Visual Odometry for vehicles equipped with calibrated stereo cameras. We combine a dense probabilistic 5D egomotion estimation method with a sparse keypoint based stereo approach to provide high quality estimates of vehicle's angular and linear velocities. To validate our approach, we perform two sets of experiments with a well known benchmarking dataset. First, we assess the quality of the raw velocity estimates in comparison to classical pose estimation algorithms. Second, we added to our method's instantaneous velocity estimates a Kalman Filter and compare its performance with a well known open source stereo Visual Odometry library. The presented results compare favorably with state-of-the-art approaches, mainly in the estimation of the angular velocities, where significant improvements are achieved.

2014

Probabilistic stereo egomotion Transform

Authors
Silva, H; Silva, E; Bernardino, A;

Publication
Proceedings - IEEE International Conference on Robotics and Automation

Abstract
In This paper we propose a novel fully probabilistic solution To The stereo egomotion estimation problem. We extend The notion of probabilistic correspondence To The stereo case which allow us To compute The whole 6D motion information in a probabilistic way. We compare The developed approach against other known state-of-the-Art methods for stereo egomotion estimation, and The obtained results compare favorably both for The linear and angular velocities estimation. © 2014 IEEE.