Please use this identifier to cite or link to this item: http://hdl.handle.net/1843/ESBF-B8VGA3
Type: Dissertação de Mestrado
Title: Boosted projections and low cost transfer learning applied to smart surveillance
Authors: Ricardo Barbosa Kloss
First Advisor: William Robson Schwartz
First Referee: Adriano Alonso Veloso
Second Referee: Silvio Jamil Ferzoli Guimarães
Abstract: 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.
Abstract: 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.
Subject: Visão por Computador
Computação
Aprendizado do computador
Teoria da estimativa
language: Inglês
Publisher: Universidade Federal de Minas Gerais
Publisher Initials: UFMG
Rights: Acesso Aberto
URI: http://hdl.handle.net/1843/ESBF-B8VGA3
Issue Date: 23-Feb-2018
Appears in Collections:Dissertações de Mestrado

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