Vision and Learning for Intelligent Human -Computer Interaction

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.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Wu, Ying

Contributors dc:contributor

• Huang, Thomas S.

• Engineering, System Science

Language dc:language

eng

Identifier

(MiAaPQ)AAI3023235

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/80744