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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

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Publications

2022

Boosting color similarity decisions using the CIEDE2000_PF Metric

Authors
Pereira, A; Carvalho, P; Côrte-Real, L;

Publication
Signal, Image and Video Processing

Abstract

2021

Automatic TV Logo Identification for Advertisement Detection without Prior Data

Authors
Carvalho, P; Pereira, A; Viana, P;

Publication
Applied Sciences

Abstract
Advertisements are often inserted in multimedia content, and this is particularly relevant in TV broadcasting as they have a key financial role. In this context, the flexible and efficient processing of TV content to identify advertisement segments is highly desirable as it can benefit different actors, including the broadcaster, the contracting company, and the end user. In this context, detecting the presence of the channel logo has been seen in the state-of-the-art as a good indicator. However, the difficulty of this challenging process increases as less prior data is available to help reduce uncertainty. As a result, the literature proposals that achieve the best results typically rely on prior knowledge or pre-existent databases. This paper proposes a flexible method for processing TV broadcasting content aiming at detecting channel logos, and consequently advertising segments, without using prior data about the channel or content. The final goal is to enable stream segmentation identifying advertisement slices. The proposed method was assessed over available state-of-the-art datasets as well as additional and more challenging stream captures. Results show that the proposed method surpasses the state-of-the-art.

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; Luis 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.

2020

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

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

Publication
Proceedings of the 28th ACM International Conference on Multimedia

Abstract

Supervised
thesis

2020

Flexible and Interactive Navigation in Synthesized Environment

Author
Vítor Magalhães

Institution
UP-FEUP

2020

Definition and adaptation of 3D templates for synthesizing human activity

Author
Ricardo Miguel Oliveira Rodrigues de Carvalho

Institution
UP-FEUP

2020

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

Author
Luís Miguel Salgado Nunes Vilaça

Institution
IPP-ISEP

2020

Deteção de publicidade em conteúdos de televisão sem informação a priori

Author
Guilherme Dias Castro

Institution
UP-FEUP

2019

Face Detection and Recognition in Unconstrained Scenarios

Author
Anabela Machado Reigoto

Institution
UP-FEUP