{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/80744"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/80744","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Vision and Learning for Intelligent Human -Computer Interaction","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. 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