We are glad to announce that our colleagues from IvisionLab, Gabriel Lefundes, and Luciano Oliveira, had a paper accepted in the 2021 Conference on Graphics, Patterns and Images (SIBGRAPI). The work is entitled “Gaze estimation via self-attention augmented convolutions” and describes a novel neural network architecture, the ARes-gaze (Attention-augmented ResNet). The work is part of Ivision’s research on biometric systems, specifically on gaze estimation.

We are pleased to announce another IvisionLab publication. Our colleagues Joao Barros and Luciano Oliveira just had a paper accepted in IEEE Intelligent Vehicles Symposium. The work is entitled “Deep speed estimation from synthetic and monocular data” and describes a solution for estimating the speed of vehicles with monocular cameras.

We are glad to announce that our colleagues from IvisionLab had two papers accepted in the 2021 Brazilian Symposium on Computing Applied to Health (SBCAS). Paulo Chagas, Luiz Souza, Rodrigo Calumby, Angelo Duarte, Washington L.C. dos-Santos and Luciano Oliveira published an article entitled Deep-learning-based membranous nephropathy classification and Monte-Carlo dropout uncertainty estimation, while Sarah Cerqueira, Ellen Aguiar, Angelo Duarte, Washington dos Santos, Luciano Oliveira and Michele Angelo published a paper entitle PathoSpotter Classifier: Um Serviço Web para Auxílio à Classificação de Lesões em Glomérulos Renais. Both papers are from the PathoSpotter project, in which the IvisionLab is highly active.

It is with great satisfaction that we announce another publication. Our colleagues from IvisionLab, Leandro Estrela, and Luciano Rebouças, had a paper accepted in IEEE Latin America Transactions. The work is entitled “A prototype of a termography equipment” and describes the conception of an open-source thermographic equipment.

We are glad to announce that our colleagues from IvisionLab, Bernardo Silva, Laís Pinhero, Luciano Rebouças, and Matheus Pithon, had a paper accepted in Conference on Graphics, Patterns, and Images (SIBGRAPI). The paper is entitled “A study on tooth segmentation and numbering using end-to-end deep neural networks,” and have performed its experiments on a new and recently released data set that is available on Github.