Advanced Search
DSpace@MIT

Adaptive models for the recognition of human gesture

Research and Teaching Output of the MIT Community

Show simple item record

dc.contributor.advisor Aaron F. Bobick and Bruce M. Blumberg. en_US
dc.contributor.author Wilson, Andrew David en_US
dc.contributor.other Massachusetts Institute of Technology. Dept. of Architecture. Program In Media Arts and Sciences. en_US
dc.date.accessioned 2011-05-23T17:51:42Z
dc.date.available 2011-05-23T17:51:42Z
dc.date.copyright 2000 en_US
dc.date.issued 2000 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/62951
dc.description Thesis (Ph.D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2000. en_US
dc.description Includes bibliographical references (leaves 135-140). en_US
dc.description.abstract Tomorrow's ubiquitous computing environments will go beyond the keyboard, mouse and monitor paradigm of interaction and will require the automatic interpretation of human motion using a variety of sensors including video cameras. I present several techniques for human motion recognition that are inspired by observations on human gesture, the class of communicative human movement. Typically, gesture recognition systems are unable to handle systematic variation in the input signal, and so are too brittle to be applied successfully in many real-world situations. To address this problem, I present modeling and recognition techniques to adapt gesture models to the situation at hand. A number of systems and frameworks that use adaptive gesture models are presented. First, the parametric hidden Markov model (PHMM) addresses the representation and recognition of gesture families, to extract how a gesture is executed. Second, strong temporal models drawn from natural gesture theory are exploited to segment two kinds of natural gestures from video sequences. Third, a realtime computer vision system learns gesture models online from time-varying context. Fourth, a realtime computer vision system employs hybrid Bayesian networks to unify and extend the previous approaches, as well as point the way for future work. en_US
dc.description.statementofresponsibility by Andrew David Wilson. en_US
dc.format.extent 140 leaves en_US
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. en_US
dc.rights.uri http://dspace.mit.edu/handle/1721.1/7582 en_US
dc.subject Architecture. Program In Media Arts and Sciences. en_US
dc.title Adaptive models for the recognition of human gesture en_US
dc.type Thesis en_US
dc.description.degree Ph.D. en_US
dc.contributor.department Massachusetts Institute of Technology. Dept. of Architecture. Program In Media Arts and Sciences. en_US
dc.identifier.oclc 48591188 en_US


Files in this item

Name Size Format Description
48591188-MIT.pdf 13.31Mb PDF Full printable version

This item appears in the following Collection(s)

Show simple item record

MIT-Mirage