Publications

Publications by CTM

2019

Insulator visual non-conformity detection in overhead power distribution lines using deep learning

Authors
Morla, RS; Cruz, R; Marotta, AP; Ramos, RP; Simas Filho, EF; Cardoso, JS;

Publication
Comput. Electr. Eng.

Abstract

2019

Editorial

Authors
Carneiro, G; Tavares, JMRS; Bradley, AP; Papa, JP; Nascimento, JC; Cardoso, JS; Lu, Z; Belagiannis, V;

Publication
Comp. Meth. in Biomech. and Biomed. Eng.: Imaging & Visualization

Abstract

2019

SpaMHMM: Sparse Mixture of Hidden Markov Models for Graph Connected Entities

Authors
Pernes, D; Cardoso, JS;

Publication
International Joint Conference on Neural Networks, IJCNN 2019 Budapest, Hungary, July 14-19, 2019

Abstract

2019

Sparse Multi-Bending Snakes

Authors
Araújo, RJ; Fernandes, K; Cardoso, JS;

Publication
IEEE Trans. Image Process.

Abstract

2019

Adversarial learning for a robust iris presentation attack detection method against unseen attack presentations

Authors
Ferreira, PM; Sequeira, AF; Pernes, D; Rebelo, A; Cardoso, JS;

Publication
2019 International Conference of the Biometrics Special Interest Group, BIOSIG 2019 - Proceedings

Abstract
Despite the high performance of current presentation attack detection (PAD) methods, the robustness to unseen attacks is still an under addressed challenge. This work approaches the problem by enforcing the learning of the bona fide presentations while making the model less dependent on the presentation attack instrument species (PAIS). The proposed model comprises an encoder, mapping from input features to latent representations, and two classifiers operating on these underlying representations: (i) the task-classifier, for predicting the class labels (as bona fide or attack); and (ii) the species-classifier, for predicting the PAIS. In the learning stage, the encoder is trained to help the task-classifier while trying to fool the species-classifier. Plus, an additional training objective enforcing the similarity of the latent distributions of different species is added leading to a 'PAI-species'-independent model. The experimental results demonstrated that the proposed regularisation strategies equipped the neural network with increased PAD robustness. The adversarial model obtained better loss and accuracy as well as improved error rates in the detection of attack and bona fide presentations. © 2019 Gesellschaft fuer Informatik.

CloseRead Abstract

2019

Editorial

Authors
Carneiro, G; Manuel, J; Tavares, RS; Bradley, AP; Papa, JP; Nascimento, JC; Cardoso, JS; Lu, Z; Belagiannis, V;

Publication
Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization

Abstract