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Sobre

Sobre

Jaime S. Cardoso, licenciado em Engenharia e Eletrotécnica e de Computadores em 1999, Mestre em Engenharia Matemática em 2005 e doutorado em Visão Computacional em 2006, todos pela Universidade do Porto. Professor Associado com agregação na Faculdade de Engenharia da Universidade do Porto (FEUP) e Investigador Sénior em 'Information Processing and Pattern Recognition' no Centro de Telecomunicações e Multimédia do INESC TEC.

A sua investigação assenta em três grandes domínios: visão computacional, "machine learning" e sistemas de suporte à decisão. A investigação em processamento de imagem e vídeo tem abordado a área de biometria, imagem médica e "video tracking" para aplicações de vigilância e desportos. O trabalho em "machine learning" foca-se na adaptação de sistemas de aprendizagem às condições desafiantes de informação visual. A ênfase dos sistemas de suporte à decisão tem sido dirigida a aplicações médicas, sempre ancoradas com a análise automática de informação visual.

É co-autor de mais de 150 artigos, dos quais mais de 50 em jornais internacionais, com mais de 2400 citações (google scholar). Foi investigador principal em 6 projectos de I&D e participou em 14 projectos de I&D, incluindo 5 projectos europeus e um contrato directo com a BBC do Reino Unido.

Tópicos
de interesse
Detalhes

Detalhes

007
Publicações

2019

Are Deep Learning Methods Ready for Prime Time in Fingerprints Minutiae Extraction?

Autores
Rebelo, A; Oliveira, T; Correia, ME; Cardoso, JS;

Publicação
Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - Lecture Notes in Computer Science

Abstract

2019

Importance of subject-dependent classification and imbalanced distributions in driver sleepiness detection in realistic conditions

Autores
Silveira, CS; Cardoso, JS; Lourenco, AL; Ahlstrom, C;

Publicação
IET INTELLIGENT TRANSPORT SYSTEMS

Abstract
The first in-depth study on the use of electrocardiogram and electrooculogram for subject-dependent classification in driver sleepiness/fatigue under realistic driving conditions is presented in this work. Since acquisitions in simulated environments may be misleading for sleepiness assessment, performing studies on road are required. For that purpose, the authors present a database resulting from a field driving study performed in the SleepEye project. Based on previous research, supervised machine learning methods are implemented and applied to 16 heart- and 25 eye-based extracted features, mostly related to heart rate variability and blink events, respectively, in order to study the influence of subject dependency in sleepiness classification, using different classifiers and dealing with imbalanced class distributions. Results showed a significantly worse performance in subject-independent classification: a decrease of similar to 40 and 20% in the detection rate of the 'sleepy' class for two and three classes, respectively. Since physiological signals are the ones that present the most individual characteristics, a subject-independent classification can be even harder to perform. Transfer learning techniques and methods for imbalanced distributions are promising approaches and need further investigation.

2019

Directional support vector machines

Autores
Pernes, D; Fernande, K; Cardoso, JS;

Publicação
Applied Sciences (Switzerland)

Abstract
Several phenomena are represented by directional-angular or periodic-data; from time references on the calendar to geographical coordinates. These values are usually represented as real values restricted to a given range (e.g., [0, 2p)), hiding the real nature of this information. In order to handle these variables properly in supervised classification tasks, alternatives to the naive Bayes classifier and logistic regression were proposed in the past. In this work, we propose directional-aware support vector machines. We address several realizations of the proposed models, studying their kernelized counterparts and their expressiveness. Finally, we validate the performance of the proposed Support Vector Machines (SVMs) against the directional naive Bayes and directional logistic regression with real data, obtaining competitive results. © 2019 by the authors.

2018

A Regression Model for Predicting Shape Deformation after Breast Conserving Surgery

Autores
Zolfagharnasab, H; Bessa, S; Oliveira, SP; Faria, P; Teixeira, JF; Cardoso, JS; Oliveira, HP;

Publicação
Sensors

Abstract

2018

The development of an automatic tool to improve perforators detection in Angio CT in DIEAP flap breast reconstruction

Autores
Mavioso, C; Correia Anacleto, JC; Vasconcelos, MA; Araujo, R; Oliveira, H; Pinto, D; Gouveia, P; Alves, C; Cardoso, F; Cardoso, J; Cardoso, MJ;

Publicação
EUROPEAN JOURNAL OF CANCER

Abstract

Teses
supervisionadas

2017

Análise Automática de Melanoma Utilizando Imagens Dermatoscópicas

Autor
Bruno Miguel Ferreira Moreira

Instituição
UP-FEUP

2017

Portuguese Sign Language Recognition

Autor
Pedro Miguel Martins Ferreira

Instituição
UP-FEUP

2017

Análise e Classificação de Imagem Hiper-espectral

Autor
Borgine Vasques Gurué

Instituição
UP-FEUP

2017

Driver’s Fatigue State Monitoring using Physiological Signals

Autor
Cláudia Sofia Alferes Ribeiro da Silva Silveira

Instituição
UP-FEUP

2017

Rotated Filters and Learning Strategies in Convolutional Neural Networks for Mammographic Lesions Detection

Autor
Eduardo Meca Castro

Instituição
UP-FEUP