Type of Publication

Thesis

Date:

7 /

2019

Status

Published

Comparison of Machine Learning Algorithms in Icon Recognition

Featured in:

MD Thesis

Authors:

Ainhoa Zaro

Abstract

The main aim of this project consists in the study and comparison of Machine Learning
algorithms using them for icon recognition in Graphic Code. Graphic Code is a new Machine Readable Code recently developed by the VisTeam (Institue of Systems and Robotics) that combines the capacity of sending a big amount of information with good aesthetic, improving the capacity when compared with QRcodes, for instance. The methods used for the comparison are the Support Vector Machine and the Convolutional Neural Network. These two algorithms have been chosen because of their ability to recognise different figures and separate them correctly.

Citation
Ainhoa Zaro (2019), Comparison of Machine Learning Algorithms in Icon Recognition. MD Thesis. University of Coimbra, 2019.

Related Content

Researcher Coordinator, VIS TEAM Leader
Post-Doc Researcher
Master Student (Erasmus)
No tagged content to show
No tagged content to show
No tagged content to show

RECENT PUBLICATIONS

Using Benford’s Law for Deepfake Detection

Authors: Miguel Leão; Nuno Gonçalves
Featured in: RECPAD - 30th Portuguese Conference on Pattern Recognition. 2024, Covilhã, Portugal

Proceedings of the 12th Iberian Conference on Pattern Recognition and Image Analysis Part I

Authors: Nuno Gonçalves; Hélder P. Oliveira; Joan Andreu Sánchez
Featured in: 12th Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2025)

Proceedings of the 12th Iberian Conference on Pattern Recognition and Image Analysis Part II

Authors: Nuno Gonçalves; Hélder P. Oliveira; Joan Andreu Sánchez
Featured in: 12th Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2025)

suggested news

Paper accepted to IJCB 2025
Prof. Nuno and VIS Team successfully organizes IbPRIA...
Four papers presented @ IbPRIA 2025

RECENT PROJECTS

FACING2 – Face Image Understanding
VISUAL-ID – Unique Visual Identities in Graphics, Images and Faces
UniqueMark

Institute of Systems and Robotics Department of Electrical and Computers Engineering University of Coimbra