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About

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

004
Publications

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

2017

BMOG: Boosted Gaussian Mixture Model with Controlled Complexity

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

Publication
Pattern Recognition and Image Analysis - Lecture Notes in Computer Science

Abstract

2016

Bio-inspired Boosting for Moving Objects Segmentation

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

Publication
IMAGE ANALYSIS AND RECOGNITION (ICIAR 2016)

Abstract
Developing robust and universal methods for unsupervised segmentation of moving objects in video sequences has proved to be a hard and challenging task. State-of-the-art methods show good performance in a wide range of situations, but systematically fail when facing more challenging scenarios. Lately, a number of image processing modules inspired in biological models of the human visual system have been explored in different areas of application. This paper proposes a bio-inspired boosting method to address the problem of unsupervised segmentation of moving objects in video that shows the ability to overcome some of the limitations of widely used state-of-the-art methods. An exhaustive set of experiments was conducted and a detailed analysis of the results, using different metrics, revealed that this boosting is more significant when challenging scenarios are faced and state-of-the-art methods tend to fail.

2016

Cognition inspired format for the expression of computer vision metadata

Authors
Castro, H; Monteiro, J; Pereira, A; Silva, D; Coelho, G; Carvalho, P;

Publication
MULTIMEDIA TOOLS AND APPLICATIONS

Abstract
Over the last decade noticeable progress has occurred in automated computer interpretation of visual information. Computers running artificial intelligence algorithms are growingly capable of extracting perceptual and semantic information from images, and registering it as metadata. There is also a growing body of manually produced image annotation data. All of this data is of great importance for scientific purposes as well as for commercial applications. Optimizing the usefulness of this, manually or automatically produced, information implies its precise and adequate expression at its different logical levels, making it easily accessible, manipulable and shareable. It also implies the development of associated manipulating tools. However, the expression and manipulation of computer vision results has received less attention than the actual extraction of such results. Hence, it has experienced a smaller advance. Existing metadata tools are poorly structured, in logical terms, as they intermix the declaration of visual detections with that of the observed entities, events and comprising context. This poor structuring renders such tools rigid, limited and cumbersome to use. Moreover, they are unprepared to deal with more advanced situations, such as the coherent expression of the information extracted from, or annotated onto, multi-view video resources. The work here presented comprises the specification of an advanced XML based syntax for the expression and processing of Computer Vision relevant metadata. This proposal takes inspiration from the natural cognition process for the adequate expression of the information, with a particular focus on scenarios of varying numbers of sensory devices, notably, multi-view video.

2016

Video Based Group Tracking and Management

Authors
Pereira, A; Familiar, A; Moreira, B; Terroso, T; Carvalho, P; Corte Real, L;

Publication
IMAGE ANALYSIS AND RECOGNITION (ICIAR 2016)

Abstract
Tracking objects in video is a very challenging research topic, particularly when people in groups are tracked, with partial and full occlusions and group dynamics being common difficulties. Hence, its necessary to deal with group tracking, formation and separation, while assuring the overall consistency of the individuals. This paper proposes enhancements to a group management and tracking algorithm that receives information of the persons in the scene, detects the existing groups and keeps track of the persons that belong to it. Since input information for group management algorithms is typically provided by a tracking algorithm and it is affected by noise, mechanisms for handling such noisy input tracking information were also successfully included. Performed experiments demonstrated that the described algorithm outperformed state-of-the-art approaches.

Supervised
thesis

2016

Video based tracking for 3D scene analysis

Author
Américo José Rodrigues Pereira

Institution
UP-FEUP

2015

Processamento de Vídeo para Promoção da Atividade Física

Author
Isaac José do Couto Lopes

Institution
UP-FEUP

2015

Seguimento de grupos de pessoas utilizando vídeo

Author
Alexandra Maria Pereira Familiar

Institution
UP-FEUP

2015

People Re-identification in Multi-camera Environments

Author
Bruno Macedo Martins Santos Moreira

Institution
UP-FEUP