Cláudio R. Jung

Lucas N. Kirsten, Cláudio R. Jung

Cell detection and tracking are paramount for bio-analysis. Recent approaches rely on the tracking-by-model evolution paradigm, which usually consists of training end-to-end deep learning models to detect and track the cells on the frames with promising results. However, such methods require extensive amounts of annotated data, which is time-consuming to obtain and often requires specialized annotators. This work proposes a new approach based on the classical tracking-by-detection paradigm that alleviates the requirement of annotated data. More precisely, it approximates the cell shapes as oriented ellipses and then uses generic-purpose oriented object detectors to identify the cells in each frame. We then rely on a global data association algorithm that explores temporal cell similarity using probability distance metrics, considering that the ellipses relate to two-dimensional Gaussian distributions. Our results show that our method can achieve detection and tracking results competitively with state-of-the-art techniques that require considerably more extensive data annotation. Our code is available at: https://github.com/LucasKirsten/Deep-Cell-Tracking-EBB.

Ana L. C. Bazzan, Anderson R. Tavares, André G. Pereira et al.

The thought-provoking analogy between AI and electricity, made by computer scientist and entrepreneur Andrew Ng, summarizes the deep transformation that recent advances in Artificial Intelligence (AI) have triggered in the world. This chapter presents an overview of the ever-evolving landscape of AI, written in Portuguese. With no intent to exhaust the subject, we explore the AI applications that are redefining sectors of the economy, impacting society and humanity. We analyze the risks that may come along with rapid technological progress and future trends in AI, an area that is on the path to becoming a general-purpose technology, just like electricity, which revolutionized society in the 19th and 20th centuries. A provocativa comparação entre IA e eletricidade, feita pelo cientista da computação e empreendedor Andrew Ng, resume a profunda transformação que os recentes avanços em Inteligência Artificial (IA) têm desencadeado no mundo. Este capítulo apresenta uma visão geral pela paisagem em constante evolução da IA. Sem pretensões de exaurir o assunto, exploramos as aplicações que estão redefinindo setores da economia, impactando a sociedade e a humanidade. Analisamos os riscos que acompanham o rápido progresso tecnológico e as tendências futuras da IA, área que trilha o caminho para se tornar uma tecnologia de propósito geral, assim como a eletricidade, que revolucionou a sociedade dos séculos XIX e XX.

Pedro H. C. Avelar, Anderson R. Tavares, Thiago L. T. da Silveira et al.

This paper presents a methodology for image classification using Graph Neural Network (GNN) models. We transform the input images into region adjacency graphs (RAGs), in which regions are superpixels and edges connect neighboring superpixels. Our experiments suggest that Graph Attention Networks (GATs), which combine graph convolutions with self-attention mechanisms, outperforms other GNN models. Although raw image classifiers perform better than GATs due to information loss during the RAG generation, our methodology opens an interesting avenue of research on deep learning beyond rectangular-gridded images, such as 360-degree field of view panoramas. Traditional convolutional kernels of current state-of-the-art methods cannot handle panoramas, whereas the adapted superpixel algorithms and the resulting region adjacency graphs can naturally feed a GNN, without topology issues.