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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

010
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; 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

2020

Object Classification for Robotic Platforms

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

Publication
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

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

Publication
Advances in Visual Computing - Lecture Notes in Computer Science

Abstract

Supervised
thesis

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

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

2019

Face Detection and Recognition in Unconstrained Scenarios

Author
Anabela Machado Reigoto

Institution
UP-FEUP