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Sobre
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Sobre

Sou natural do distrito de porto. Obtive a Licenciatura em Eng. Eletrotécnica e de Computadores em 2001, o grau de Mestre em Redes e Serviços de Comunicação em 2004 e o Doutoramento em Eng. Eletrotécnica e de Computadores em 2012, todos na Faculdade de Engenharia da Universidade do Porto (FEUP). Sou colaborador no INESC TEC desde 2001 e tenho a função de Investigador Sénior no Centro de Telecomunicações e Multimédia. Sou também Professor Adjunto Convidado no Departamento de Engenharia Eletrotécnica do Instituto Superior de Engenharia do Porto (ISEP). Os meus atuais interesses de investigação incluem procesamento de imagem e vídeo, sistemas multimédia e visão computacional. 

Tópicos
de interesse
Detalhes

Detalhes

011
Publicações

2020

Efficient CIEDE2000-based Color Similarity Decision for Computer Vision

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

Publicação
IEEE Transactions on Circuits and Systems for Video Technology

Abstract

2020

Texture collinearity foreground segmentation for night videos

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

Publicação
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

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

Publicação
Advances in Visual Computing - Lecture Notes in Computer Science

Abstract

2019

Stereo vision system for human motion analysis in a rehabilitation context

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

Publicação
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

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

Publicação
Pattern Analysis and Applications

Abstract

Teses
supervisionadas

2018

Thermal imaging for vehicle occupant monitoring

Autor
Gustavo Rocha da Silva

Instituição
UP-FEUP

2018

Análise multimodal para deteção e caracterização de pessoas

Autor
Filipa Raquel da Silva Rocha

Instituição
UP-FEUP

2018

Video Based tracking for 3D Scene Analysis

Autor
Américo José Rodrigues Pereira

Instituição
UP-FEUP

2018

Deteção de Publicidade em Conteúdos de Difusão

Autor
Tiago José Rodrigues Pereira

Instituição
UP-FEUP

2018

Passenger ID and fitness monitoring using thermal images

Autor
Rui Paulo Araújo Monteiro

Instituição
UP-FEUP