Files in this item



application/pdf3023235.pdf (9MB)Restricted to U of Illinois
(no description provided)PDF


Title:Vision and Learning for Intelligent Human -Computer Interaction
Author(s):Wu, Ying
Doctoral Committee Chair(s):Huang, Thomas S.
Department / Program:Electrical Engineering
Discipline:Electrical Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Engineering, System Science
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.
Issue Date:2001
Description:161 p.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2001.
Other Identifier(s):(MiAaPQ)AAI3023235
Date Available in IDEALS:2015-09-25
Date Deposited:2001

This item appears in the following Collection(s)

Item Statistics