1 citations · 2 across the 5 of their papers we have counts for
6 papers · 1 filter
Light In The Black: An Evaluation of Data Augmentation Techniques for COVID-19 CT's Semantic Segmentation
Bruno A. Krinski, Daniel V. Ruiz, Eduardo Todt
With the COVID-19 global pandemic, computer-assisted diagnoses of medical images have gained much attention, and robust methods of Semantic Segmentation of Computed Tomography (CT)…
BEyond observation: an approach for ObjectNav
Daniel V. Ruiz, Eduardo Todt
With the rise of automation, unmanned vehicles became a hot topic both as commercial products and as a scientific research topic. It composes a multi-disciplinary field of robotics…
Can Giraffes Become Birds? An Evaluation of Image-to-image Translation for Data Generation
Daniel V. Ruiz, Gabriel Salomon, Eduardo Todt
There is an increasing interest in image-to-image translation with applications ranging from generating maps from satellite images to creating entire clothes' images from only cont…
ANDA: A Novel Data Augmentation Technique Applied to Salient Object Detection
Daniel V. Ruiz, Bruno A. Krinski, Eduardo Todt
In this paper, we propose a novel data augmentation technique (ANDA) applied to the Salient Object Detection (SOD) context. Standard data augmentation techniques proposed in the li…
Masking Salient Object Detection, a Mask Region-based Convolutional Neural Network Analysis for Segmentation of Salient Objects
Bruno A. Krinski, Daniel V. Ruiz, Guilherme Z. Machado +1
In this paper, we propose a broad comparison between Fully Convolutional Networks (FCNs) and Mask Region-based Convolutional Neural Networks (Mask-RCNNs) applied in the Salient Obj…
An Efficient and Layout-Independent Automatic License Plate Recognition System Based on the YOLO detector
Rayson Laroca, Luiz A. Zanlorensi, Gabriel R. Gonçalves +3
This paper presents an efficient and layout-independent Automatic License Plate Recognition (ALPR) system based on the state-of-the-art YOLO object detector that contains a unified…