Boosted projections and low cost transfer learning applied to smart surveillance

Detalhes bibliográficos
Ano de defesa: 2018
Autor(a) principal: Ricardo Barbosa Kloss
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: eng
Instituição de defesa: Universidade Federal de Minas Gerais
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Visão por Computador
Computação
Aprendizado do computador
Teoria da estimativa
Dimensionality Reduction
Computer Vision
Machine Learning
Deep Learning
Link de acesso: https://hdl.handle.net/1843/ESBF-B8VGA3
Resumo: Computer vision is an important area related to understanding the world through images. It can be used in biometrics, by verifying whether a given face is of a certain identity, used to look for crime perpetrators in an airport blacklist, used in human-machine interactions and other goals. Deep learning methods have become ubiquitous in computer vision achieving many breakthroughs, making possible for machines, for instance, to verify if two photos belong to the same person with human-level skill. This work tackles two computer vision problems applied to surveillance. First, we explore deep learning methods for computer vision in the task of face verification and second, we explore dimensionality reduction techniques for the task of detection.
id UFMG_a84e2a2a2a4c37176c874424680c0792
oai_identifier_str oai:repositorio.ufmg.br:1843/ESBF-B8VGA3
network_acronym_str UFMG
network_name_str Repositório Institucional da UFMG
repository_id_str
spelling Boosted projections and low cost transfer learning applied to smart surveillanceVisão por ComputadorComputaçãoAprendizado do computadorTeoria da estimativaDimensionality ReductionComputer VisionMachine LearningDeep LearningComputer vision is an important area related to understanding the world through images. It can be used in biometrics, by verifying whether a given face is of a certain identity, used to look for crime perpetrators in an airport blacklist, used in human-machine interactions and other goals. Deep learning methods have become ubiquitous in computer vision achieving many breakthroughs, making possible for machines, for instance, to verify if two photos belong to the same person with human-level skill. This work tackles two computer vision problems applied to surveillance. First, we explore deep learning methods for computer vision in the task of face verification and second, we explore dimensionality reduction techniques for the task of detection.Universidade Federal de Minas Gerais2019-08-13T20:49:42Z2025-09-08T23:26:51Z2019-08-13T20:49:42Z2018-02-23info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/1843/ESBF-B8VGA3Ricardo Barbosa Klossinfo:eu-repo/semantics/openAccessengreponame:Repositório Institucional da UFMGinstname:Universidade Federal de Minas Gerais (UFMG)instacron:UFMG2025-09-08T23:26:51Zoai:repositorio.ufmg.br:1843/ESBF-B8VGA3Repositório InstitucionalPUBhttps://repositorio.ufmg.br/oairepositorio@ufmg.bropendoar:2025-09-08T23:26:51Repositório Institucional da UFMG - Universidade Federal de Minas Gerais (UFMG)false
dc.title.none.fl_str_mv Boosted projections and low cost transfer learning applied to smart surveillance
title Boosted projections and low cost transfer learning applied to smart surveillance
spellingShingle Boosted projections and low cost transfer learning applied to smart surveillance
Ricardo Barbosa Kloss
Visão por Computador
Computação
Aprendizado do computador
Teoria da estimativa
Dimensionality Reduction
Computer Vision
Machine Learning
Deep Learning
title_short Boosted projections and low cost transfer learning applied to smart surveillance
title_full Boosted projections and low cost transfer learning applied to smart surveillance
title_fullStr Boosted projections and low cost transfer learning applied to smart surveillance
title_full_unstemmed Boosted projections and low cost transfer learning applied to smart surveillance
title_sort Boosted projections and low cost transfer learning applied to smart surveillance
author Ricardo Barbosa Kloss
author_facet Ricardo Barbosa Kloss
author_role author
dc.contributor.author.fl_str_mv Ricardo Barbosa Kloss
dc.subject.por.fl_str_mv Visão por Computador
Computação
Aprendizado do computador
Teoria da estimativa
Dimensionality Reduction
Computer Vision
Machine Learning
Deep Learning
topic Visão por Computador
Computação
Aprendizado do computador
Teoria da estimativa
Dimensionality Reduction
Computer Vision
Machine Learning
Deep Learning
description Computer vision is an important area related to understanding the world through images. It can be used in biometrics, by verifying whether a given face is of a certain identity, used to look for crime perpetrators in an airport blacklist, used in human-machine interactions and other goals. Deep learning methods have become ubiquitous in computer vision achieving many breakthroughs, making possible for machines, for instance, to verify if two photos belong to the same person with human-level skill. This work tackles two computer vision problems applied to surveillance. First, we explore deep learning methods for computer vision in the task of face verification and second, we explore dimensionality reduction techniques for the task of detection.
publishDate 2018
dc.date.none.fl_str_mv 2018-02-23
2019-08-13T20:49:42Z
2019-08-13T20:49:42Z
2025-09-08T23:26:51Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://hdl.handle.net/1843/ESBF-B8VGA3
url https://hdl.handle.net/1843/ESBF-B8VGA3
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidade Federal de Minas Gerais
publisher.none.fl_str_mv Universidade Federal de Minas Gerais
dc.source.none.fl_str_mv reponame:Repositório Institucional da UFMG
instname:Universidade Federal de Minas Gerais (UFMG)
instacron:UFMG
instname_str Universidade Federal de Minas Gerais (UFMG)
instacron_str UFMG
institution UFMG
reponame_str Repositório Institucional da UFMG
collection Repositório Institucional da UFMG
repository.name.fl_str_mv Repositório Institucional da UFMG - Universidade Federal de Minas Gerais (UFMG)
repository.mail.fl_str_mv repositorio@ufmg.br
_version_ 1856414022715310080

Registros relacionados