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About

I was born in the district of Porto. I got a degree in Eletric and Computer Engeneering in 2001, a Master degre in Networks and Communication Services in 2004 and the PhD degree in Eletric and COmputer Engeneering in 2012, all from the Faculty of Engeneering of the University of Porto. I've been a collaborator of INESC TEC since 2001 and I'm currently a Senior Researcher at the Center of Telecommunications and Multimedia. I'm also an Invited Adjunct Professor at the School f Engeneering of the Polythecnic Institute of Porto. My current reseach interests include image and video processing, multimedia systems and computer vision. 

Interest
Topics
Details

Details

011
Publications

2020

Efficient CIEDE2000-based Color Similarity Decision for Computer Vision

Authors
Pereira, A; Carvalho, P; Coelho, G; Corte Real, L;

Publication
IEEE Transactions on Circuits and Systems for Video Technology

Abstract

2020

Texture collinearity foreground segmentation for night videos

Authors
Martins, I; Carvalho, P; Corte Real, L; Alba Castro, JL;

Publication
Computer Vision and Image Understanding

Abstract
One of the most difficult scenarios for unsupervised segmentation of moving objects is found in nighttime videos where the main challenges are the poor illumination conditions resulting in low-visibility of objects, very strong lights, surface-reflected light, a great variance of light intensity, sudden illumination changes, hard shadows, camouflaged objects, and noise. This paper proposes a novel method, coined COLBMOG (COLlinearity Boosted MOG), devised specifically for the foreground segmentation in nighttime videos, that shows the ability to overcome some of the limitations of state-of-the-art methods and still perform well in daytime scenarios. It is a texture-based classification method, using local texture modeling, complemented by a color-based classification method. The local texture at the pixel neighborhood is modeled as an N-dimensional vector. For a given pixel, the classification is based on the collinearity between this feature in the input frame and the reference background frame. For this purpose, a multimodal temporal model of the collinearity between texture vectors of background pixels is maintained. COLBMOG was objectively evaluated using the ChangeDetection.net (CDnet) 2014, Night Videos category, benchmark. COLBMOG ranks first among all the unsupervised methods. A detailed analysis of the results revealed the superior performance of the proposed method compared to the best performing state-of-the-art methods in this category, particularly evident in the presence of the most complex situations where all the algorithms tend to fail. © 2020 Elsevier Inc.

2019

Face Detection in Thermal Images with YOLOv3

Authors
Silva, G; Monteiro, R; Ferreira, A; Carvalho, P; Corte-Real, L;

Publication
Advances in Visual Computing - Lecture Notes in Computer Science

Abstract

2019

Stereo vision system for human motion analysis in a rehabilitation context

Authors
Matos, AC; Terroso, TA; Corte Real, L; Carvalho, P;

Publication
Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization

Abstract
The present demographic trends point to an increase in aged population and chronic diseases which symptoms can be alleviated through rehabilitation. The applicability of passive 3D reconstruction for motion tracking in a rehabilitation context was explored using a stereo camera. The camera was used to acquire depth and color information from which the 3D position of predefined joints was recovered based on: kinematic relationships, anthropometrically feasible lengths and temporal consistency. Finally, a set of quantitative measures were extracted to evaluate the performed rehabilitation exercises. Validation study using data provided by a marker based as ground-truth revealed that our proposal achieved errors within the range of state-of-the-art active markerless systems and visual evaluations done by physical therapists. The obtained results are promising and demonstrate that the developed methodology allows the analysis of human motion for a rehabilitation purpose. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.

2018

BMOG: boosted Gaussian Mixture Model with controlled complexity for background subtraction

Authors
Martins, I; Carvalho, P; Corte Real, L; Alba Castro, JL;

Publication
Pattern Analysis and Applications

Abstract

Supervised
thesis

2018

Passenger ID and fitness monitoring using thermal images

Author
Rui Paulo Araújo Monteiro

Institution
UP-FEUP

2018

Análise Perceptual Automática de Imagens de Fotojornalismo

Author
Tiago Lima Dias Lavarinhas

Institution
UP-FEUP

2018

Thermal imaging for vehicle occupant monitoring

Author
Gustavo Rocha da Silva

Institution
UP-FEUP

2018

Análise multimodal para deteção e caracterização de pessoas

Author
Filipa Raquel da Silva Rocha

Institution
UP-FEUP

2018

Video Based tracking for 3D Scene Analysis

Author
Américo José Rodrigues Pereira

Institution
UP-FEUP