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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

010
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; Luis 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.

2020

Semantic Storytelling Automation: A Context-Aware and Metadata-Driven Approach

Autores
Viana, P; Carvalho, P; Andrade, MT; Jonker, PP; Papanikolaou, V; Teixeira, IN; Vilaça, L; Pinto, JP; Costa, T;

Publicação
Proceedings of the 28th ACM International Conference on Multimedia

Abstract

2020

Object Classification for Robotic Platforms

Autores
Brandenburg, S; Machado, P; Shinde, P; Ferreira, JF; McGinnity, TM;

Publicação
FOURTH IBERIAN ROBOTICS CONFERENCE: ADVANCES IN ROBOTICS, ROBOT 2019, VOL 2

Abstract
Computer vision has been revolutionised in recent years by increased research in convolutional neural networks (CNNs); however, many challenges remain to be addressed in order to ensure fast and accurate image processing when applying these techniques to robotics. These challenges consist of handling extreme changes in scale, illumination, noise, and viewing angles of a moving object. The project main contribution is to provide insight on how to properly train a convolutional neural network (CNN), a specific type of DNN, for object tracking in the context of industrial robotics. The proposed solution aims to use a combination of documented approaches to replicate a pick-and-place task with an industrial robot using computer vision feeding a YOLOv3 CNN. Experimental tests, designed to investigate the requirements of training the CNN in this context, were performed using a variety of objects that differed in shape and size in a controlled environment. The general focus was to detect the objects based on their shape; as a result, a suitable and secure grasp could be selected by the robot. The findings in this article reflect the challenges of training the CNN through brute force. It also highlights the different methods of annotating images and the ensuing results obtained after training the neural network.

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

Teses
supervisionadas

2020

Towards a Scalable Dataset Construction for Facial Recognition: A guided data selection approach for diversity stimulation

Autor
Luís Miguel Salgado Nunes Vilaça

Instituição
IPP-ISEP

2020

Deteção de publicidade em conteúdos de televisão sem informação a priori

Autor
Guilherme Dias Castro

Instituição
UP-FEUP

2020

Flexible and Interactive Navigation in Synthesized Environment

Autor
Vítor Magalhães

Instituição
UP-FEUP

2020

Definition and adaptation of 3D templates for synthesizing human activity

Autor
Ricardo Miguel Oliveira Rodrigues de Carvalho

Instituição
UP-FEUP

2019

Face Detection and Recognition in Unconstrained Scenarios

Autor
Anabela Machado Reigoto

Instituição
UP-FEUP