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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

009
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

2018

GymApp: A real time physical activity trainner on wearable devices

Authors
Viana, P; Ferreira, T; Castro, L; Soares, M; Pinto, JP; Andrade, T; Carvalho, P;

Publication
Proceedings - 2018 11th International Conference on Human System Interaction, HSI 2018

Abstract
Technological advances are pushing into the mass market innovative wearable devices featuring increasing processing and sensing capacity, non-intrusiveness and ubiquitous use. Sensors built-in those devices, enable acquiring different types of data and by taking advantage of the available processing power, it is possible to run intelligent applications that process the sensed data to offer added-value to the user in multiple domains. Although not new to the modern society, it is unquestionable that the present exercise boom is rapidly spreading across all age groups. However, in a great majority of cases, people perform their physical activity on their own, either due to time or budget constraints and may easily get discouraged if they do not see results or perform exercises inadequately. This paper presents an application, running on a wearable device, aiming at operating as a personal trainer that validates a set of proposed exercises in a sports session. The developed solution uses inertial sensors of an Android Wear smartwatch and, based on a set of pattern recognition algorithms, detects the rate of success in the execution of a planned workout. The fact that all processing can be executed on the device is a differentiator factor to other existing solutions. © 2018 IEEE.

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.

Supervised
thesis

2018

Optimizing customer response to direct marketing initiatives

Author
Francisco António Dias Amorim

Institution
UP-FEUP

2017

Image Processing for Event Detection in Retail Environments

Author
Pedro Daniel Nunes Querido

Institution
UP-FEUP

2015

People Re-identification in Multi-camera Environments

Author
Bruno Macedo Martins Santos Moreira

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