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Doctoral Thesis
DOI
https://doi.org/10.11606/T.18.2011.tde-19102011-110608
Document
Author
Full name
Lucio André de Castro Jorge
Institute/School/College
Knowledge Area
Date of Defense
Published
São Carlos, 2011
Supervisor
Committee
Obac Roda, Valentin (President)
Durand, Adolfo Nicolas Posadas
Gonzaga, Adilson
Luppe, Maximiliam
Traina, Agma Juci Machado
Title in Portuguese
Metodologia de fusão de vídeos e sons para monitoração de comportamento de insetos
Keywords in Portuguese
Fusão de sensores
Seleção de características
Wavelet-multifractal
Abstract in Portuguese
Este trabalho apresenta uma nova abordagem para fusão de vídeo e som diretamente no espaço de atributos visando otimizar a identificação do comportamento de insetos. Foi utilizado o detector de Harris para rastreamento dos insetos, assim como a técnica inovadora Wavelet-Multifractal para análise de som. No caso da Wavelet-Multifractal, foram testadas várias Wavelet-mães, sendo a Morlet a melhor escolha para sons de insetos. Foi proposto a Wavelet Módulo Máximo para extrair atributos multifractais dos sons para serem utilizados no reconhecimento de padrões de comportamento de insetos. A abordagem Wrapper de mineração de dados foi usada para selecionar os atributos relevantes. Foi constatado que a abordagem Wavelet-multifractal identifica melhor os sons, particularmente no caso de distorções provocadas por ruídos. As imagens foram responsáveis pela identificação de acasalamento e os sons pelos outros comportamentos. Foi também proposto um novo método do triângulo como representação simplificada do espectro multifractal visando simplificação do processamento.
Title in English
Merging methodology videos and sounds for monitoring insect behavior
Keywords in English
Feature selection
Fusion
Wavelet-multifractal
Wrapper
Abstract in English
This work presents an innovative video and sound fusion approach by feature subset selection under the space of attributes to optimally identify insects behavior. Harris detector was used for insect movement tracking and an innovative technique of Multifractal-Wavelet was used to analyze the insect sounds. In the case of Multifractal-Wavelet, more than one mother-wavelet was tested, being the Morlet wavelet the best choice of mother-wavelet for insect sounds. The wavelet modulus maxima was proposed to extract multifractal sound attributes to be used in pattern recognition of an insect behavior. The wrapper data mining approach was used to select relevant attributes. It has been found that, in general, wavelet-multifractal based schemes perform better for sound, particularly in terms of minimizing noise distortion influence. The image features only determine the mating and the sound other behaviors. A new triangle representation of multifractal spectrum was proposed as a processing simplification.
 
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Lucio.pdf (6.29 Mbytes)
Publishing Date
2011-10-25
 
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