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

Feature-based Identity Unlearning Evaluation for Biometric Applications

Authors: Iurii Medvedev; Ajnas Muhammed; Nuno Gonçalves
Featured in: 14th International Workshop on Biometrics and Forensics

Detection and Distortion Correction of Arbitrary 2D Barcodes

Authors: Allan Freitas; João Marcos; Nuno Gonçalves
Featured in: Submitted to Computers & Industrial Engineering journal

FLOWING: Implicit Neural Flows for Structure-Preserving Morphing

Authors: Arthur Bizzi; Matias Grynberg; Vitor Matias; Daniel Perazzo; João Paulo Lima; Luiz Velho; Nuno Gonçalves; João Pereira; Guilherme Schardong; Tiago Novello
Featured in: 39th Conference on Neural Information Processing Systems (NeurIPS 2025)

suggested news

VIS Team will host a session presenting the...
Paper presented at NeurIPS 2025
VIS Team has presented workshop at the 12th...

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