Dynamic Bayesian Networks for Information Fusion With Applications to Human-Computer Interfaces
Pavlovic, Vladimir Ivan
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https://hdl.handle.net/2142/81285
Description
Title
Dynamic Bayesian Networks for Information Fusion With Applications to Human-Computer Interfaces
Author(s)
Pavlovic, Vladimir Ivan
Issue Date
1999
Doctoral Committee Chair(s)
Huang, Thomas S.
Department of Study
Electrical Engineering
Discipline
Electrical Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Computer Science
Language
eng
Abstract
Recent advances in various display and virtual technologies coupled with an explosion in available computing power have given rise to a number of novel human-computer interaction (HCI) modalities---speech, vision-based gesture recognition, eye tracking, EEG, etc. However, despite the abundance of novel interaction devices, the naturalness and efficiency of HCI has remained low. This is due in particular to the lack of robust sensory data interpretation techniques. To deal with the task of interpreting single and multiple interaction modalities this dissertation establishes a novel probabilistic approach based on dynamic Bayesian networks (DBNs). As a generalization of the successful hidden Markov models, DBNs are a natural basis for the general temporal action interpretation task. The problem of interpretation of single or multiple interacting modalities can then be viewed as a Bayesian inference task. In this work three complex DBN models are introduced: mixtures of DBNs, mixed-state DBNs, and coupled HMMs. In-depth study of these models yields efficient approximate inference and parameter learning techniques applicable to a wide variety of problems. Experimental validation of the proposed approaches in the domains of gesture and speech recognition confirms the model's applicability to both unimodal and multimodal interpretation tasks.
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