Skills

Python

Image processing

ML packages

Blockchain

Scrum

DevOps

Experience

 
 
 
 
 
January 2018 – January 2019
Porto - Portugal

Computer Vision Research Scientist

GTP Automation

Computer Vision and Deep Learning development for Industry 4.0 solutions:

  • Vision Picking: Package classification based on AR and CNN’s.
  • Volumes Estimation: Calculation the volume of shipping container racks with aerial footage, using OpenCV and shapefile format.
  • Drone Indoor Positioning: Estimate position using printed ArUco markers.
 
 
 
 
 
February 2015 – Present
Porto - Portugal

Ph.D. Candidate

CNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico

Ph.D. fellow of the CNPq in the Faculty of Engineering at University of Porto. I am is carrying out a project in Segmentation of Skin in dermatoscopic images using SuperPixels combined with Complex Networks.
 
 
 
 
 
January 2013 – December 2014
Mato Grosso - Brazil

Professor

Mato Grosso State University

Professor and Researcher in the disciplinary area of Computer Science, with focus on the following curricular units:

  • algorithms;
  • data structure;
  • computer graphics;
 
 
 
 
 
August 2011 – December 2012
São Paulo - Brazil

Professor

Faculdade Anhanguera

Professor and Researcher in the disciplinary area of Computer Science, with focus on the following curricular units: algorithms, data structure, and computer graphics;
 
 
 
 
 
February 2010 – December 2012
São Paulo - Brazil

Professor

Faculdade Orígenes Lessa

Professor and Researcher in the disciplinary area of Computer Science, with focus on the following curricular units: algorithms, data structure, and computer graphics;

Selected Publications

Image segmentation is one of the most important tasks in image analysis. This task can be used on an extensive range of application. However, some traditional techniques exhibit high computational costs, hindering their application because the complexity of such approaches is intrinsically related to the nature of the image. This work proposal explores a new area of application of the theory of complex networks until then little explored, the analysis of images. It is a relatively recent area of study that has attracted the attention of the scientific community and has been successfully applied to a large scale in the analysis of real-world problems.
In USNCCM, July 28-August 1, 2019

Studies of complex networks have been an important topic of interest to many researchers, in part due to its potential for a simple representation of complex systems in various fields of science. This work aims to develop and study some methods used for the characterization of complex networks. Thus, a new approach to the dermatoscopic image analysis that combines extraction of superpixels and detection of communities of a complex network is expected to be developed. To reduce the computational cost, a SLIC preprocessing algorithm will be used to group several pixels of the image into a uniform region (superpixels), which will decrease the size of the network and, consequently, minimize the computational cost of the cluster.
In CMBBE, 2018

In this paper, the study and application of data analysis techniques for extracting information is proposed. The contribution of this work targets the process of identification of relevant literature from a collection of crawled documents. Novel functions, called social network features, are described and evaluated on documents crawled on ArXiv, to examine their relevance. The results highlight the data analysis process and the performance of the classification of the data mining algorithms used.
In RECPAD, 2017

The theory Compressive Sensing (CS) has provided a new acquisition strategy and recovery with good in the image processing area. This theory guarantees to recover a signal with high probability from a reduced sampling rate below the Nyquist-Shannon limit. The problem of recovering the original signal from the samples consists of solving an optimization problem. This article presents an overview of reconstruction algorithms for sparse signal recovery in CS, these algorithms may be broadly divided into six types. We have provided a comprehensive survey of the numerous reconstruction algorithms in CS aiming to achieve computational efficiency.
In DSIE|16, 2016

In the pattern recognition research field, Support Vector Machines (SVM) have been an effectiveness tool for classification purposes, being successively employed in many applications. The SVM input data is transformed into a high dimensional space using some kernel functions where linear separation is more likely. However, there are some computational drawbacks associated to SVM. One of them is the computational burden required to find out the more adequate parameters for the kernel mapping considering each non-linearly separable input data space, which reflects the performance of SVM. This paper introduces the Polynomial-Powers of Sigmoid for SVM kernel mapping, and it shows their advantages over well-known kernel functions using real and synthetic datasets.
In SIBGRAPI, 2014

Accomplish­ments

Feb 2019

Blockchain Fundamentals

edX - In starting

Jan 2019

Data Science

Coursera - Specialization

Jan 2019

Neural Networks and Deep Learning

Coursera - In Progress

Recent Publications

More Publications

Image segmentation is one of the most important tasks in image analysis. This task can be used on an extensive range of application. …

Studies of complex networks have been an important topic of interest to many researchers, in part due to its potential for a simple …

In this paper, the study and application of data analysis techniques for extracting information is proposed. The contribution of this …

The theory Compressive Sensing (CS) has provided a new acquisition strategy and recovery with good in the image processing area. This …

Projects

Shopper Tracking

Tracking people and product interaction, to quantify store performance and analyze (anonymous) customer behavior.

Computer Vision Projects

Various computer vision tasks

Data Science Projects

This repo contains a curated list of Python studies and tutorials for Data Science, NLP, and Machine Learning

Vision Picking

Innovative hands-free order picking

Marketplace Energy

Blockchain Project: Marketplace Energy

Synthetic Images

Synthetic Images Project: Building datasets

Contact