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About

About

I was born in Porto, Portugal, in 1990. I graduated in Electrical and Computers Engineering (Master's Degree) at FEUP (Faculty of Engineering, University of Porto) in 2014.

My first collaboration with INESC TEC happened in 2013, when I joined the VCMI group (Visual Computing and Machine Intelligence) to work in the development of a software tool for breast surgery planning.

From 2014 to 2016, I worked at Synopsys, Inc. as an ASIC Digital Design Engineer. The main purpose of my work was the development of System Verilog verification environments for specific interface intelectual property standards.

I came back to INESC TEC and to the VCMI group in January 2017. Right now, my research is mainly focused on Machine Learning foundamental topics, with some application to Computer Vision.

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Details

Details

002
Publications

2019

Directional support vector machines

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

Publication
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.

2014

Fitting of superquadrics for breast modelling by geometric distance minimization

Authors
Pernes, D; Cardoso, JS; Oliveira, HP;

Publication
2014 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2014, Belfast, United Kingdom, November 2-5, 2014

Abstract
Breast cancer is one of the most mediated malignant diseases, because of its high incidence and prevalence, but principally due to its physical and psychological invasiveness. Surgeons and patients have often many options to consider for undergoing the procedure. The ability to visualise the potential outcomes of the surgery and make decisions on their surgical options is, therefore, very important for patients and surgeons. In this paper we investigate the fitting of a 3d point cloud of the breast to a parametric model usable in surgery planning, obtaining very promising results with real data. © 2014 IEEE.