Vision and Learning for Intelligent Human -Computer Interaction
Wu, Ying
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https://hdl.handle.net/2142/80744
Description
Title
Vision and Learning for Intelligent Human -Computer Interaction
Author(s)
Wu, Ying
Issue Date
2001
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)
Engineering, System Science
Language
eng
Abstract
This dissertation presents three effective techniques for visual motion analysis tasks: non-stationary color model adaptation for efficient localization, multiple visual cues integration for robust tracking, and learning motion models for capturing articulated hand motion. Besides, this dissertation describes a novel statistical learning method, the Discriminant-EM (D-EM) algorithm, in the framework of self-supervised learning paradigm. D-EM employs both labeled and unlabeled training data and converges supervised and unsupervised learning. Many topics in the dissertation is unified by the four problems of self-supervised learning, i.e., transduction, co-transduction, model transduction and co-inferencing. Extensive experiments and two prototype systems have validated the proposed approaches in the domain of vision-based human computer interaction.
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