aluno: tiago andrade togores orientador: professor flávio ... · l. r. rabiner. a tutorial on...
TRANSCRIPT
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Aluno: Tiago Andrade TogoresOrientador: Professor Flávio Soares Côrrea da Silva
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Interação humano-computador
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Interação humano-computador Interfaces naturais
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Interação humano-computador Interfaces naturais Popularização
Wii
Smartphones
Tablets
Kinect
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Desenvolvimento de um programa que
reconheça gestos
treine gestos
alcance usuários comuns
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Desenvolvimento de um programa que
reconheça gestos -> inteligência artificial
treine gestos -> aprendizado computacional
alcance usuários comuns -> Windows
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Câmera RGB
Resolução: 640x480
Taxa de atualização: 30 fps
Field of view (vertical ): 43º
Field of view (horizontal): 57º
Stream: 32-bit
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Sensor de profundidade
Resolução: 640x480
Taxa de atualização: 30 fps
Field of view (vertical ): 43º
Field of view (horizontal): 57º
Stream: 16-bit
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Microfones
Motor
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Open Natural Interaction
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Organização
Interoperabilidade:
Dispositivos
Aplicações
Middleware
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API - camada intermediária Production Chain Multi-plataforma Várias linguagens Gravação dados Uso com diferentes dispositivos
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Middleware Visão computacional
Detecção de usuários Rastreamento 8 ativos
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Model-View-Presenter (Passive View)
ControllerViewsDevice
NUIUsers
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Características:
Posição
Orientação
Velocidade
Aceleração
Escolha delas é MUITO IMPORTANTE
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Informação Redundante
Representação mínima
Filtro de semelhança
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Mapeamento de vetores para números inteiros
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Mapeamento de vetores para números inteiros
Particionar o espaço
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Algoritmo K-Médias
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Algoritmo K-Médias
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Algoritmo K-Médias
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Algoritmo K-Médias
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Cada modelo representa um gesto
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Cada modelo representa um gesto
Processo duplamente estocástico Estados ocultos variam com o tempo Observações emitidas pelo estados Parâmetros conhecidos Propriedade de Markov
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Ergódica
π
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Bakis
π
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Chuva Sol
π
FazerCompras
Andar Ler
configuração inicialestados
observações
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Estimação
Decodificação
Aprendizagem
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Estimação -> Reconhecimento
Decodificação
Aprendizagem -> Treinamento
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Problema: computar eficientemente a probabilidade de uma sequência de observações dado um HMM
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Variável Forward:
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Variável Forward:
Por Indução:
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Variável Backward:
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Variável Backward:
Por Indução:
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Solução:
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Solução:
Complexidade:
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Temos
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Temos
Queremos
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Temos
Queremos
Bayes:
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Temos
Queremos
Bayes:
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Problema: Achar um novo modelo a partir do existente que maximize (localmente) a probabilidade de se obter uma sequência observada
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Mais uma variável:
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Mais uma variável:
Mas o que ela representa?
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Mais outra variável:
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Mais outra variável:
Mas o que ela representa?
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Solução:
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Solução:
Complexidade por iteração:
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Problemas resolvidos
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Problemas resolvidos
Underflow!
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Problemas resolvidos
Underflow!
Escalar probabilidades
Usar valor do logaritmo
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Problemas resolvidos
Underflow!
Escalar probabilidades
Usar valor do logaritmo
Treinamento adequado exige múltiplas observações!
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4 pessoas 4 gestos 160 execuções de gesto
80 para treinamento
80 para reconhecimento
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Diferentes módulos
Inicialização do quantizador
Extratores
Níveis de filtragem
HMM
▪ Topologia
▪ Número de estados
▪ Número de símbolos
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Variação da porcentagem de acerto Necessidade de grande conjunto de teste Escolha dos melhores parâmetros
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L. R. Rabiner. A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2):257-286, 1989.
VM. Mantyla. Discrete hidden markov models with application to isolated user-dependent hand gesture recognition. VTT publications, 2001.
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