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Understanding expressive action

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dc.contributor.advisor Alex P. Pentland. en_US Wren, Christopher R. (Christopher Richard) en_US
dc.contributor.other Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. en_US 2005-05-19T14:32:45Z 2005-05-19T14:32:45Z 2000 en_US 2000 en_US
dc.identifier.uri en_US
dc.description Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000. en_US
dc.description Also available online at the MIT Theses Online homepage <> en_US
dc.description Includes bibliographical references (p. 117-120). en_US
dc.description This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. en_US
dc.description.abstract We strain our eyes, cramp our necks, and destroy our hands trying to interact with computer on their terms. At the extreme, we strap on devices and weigh ourselves down with cables trying to re-create a sense of place inside the machine, while cutting ourselves off from the world and people around us. The alternative is to make the real environment responsive to our actions. It is not enough for environments to respond simply to the presence of people or objects: they must also be aware of the subtleties of changing situations. If all the spaces we inhabit are to be responsive, they must not require encumbering devices to be worn and they must be adaptive to changes in the environment and changes of context. This dissertation examines a body of sophisticated perceptual mechanisms developed in response to these needs as well as a selection of human-computer interface sketches designed to push the technology forward and explore the possibilities of this novel interface idiom. Specifically, the formulation of a fully recursive framework for computer vision called DYNA that improves performance of human motion tracking will be examined in depth. The improvement in tracking performance is accomplished with the combination of a three-dimensional, physics-based model of the human body with modifications to the pixel classification algorithms that enable them to take advantage of this high-level knowledge. The result is a novel vision framework that has no completely bottom-up processes, and is therefore significantly faster and more stable than other approaches. en_US
dc.description.statementofresponsibility by Christopher R. Wren. en_US
dc.format.extent 120 p. en_US
dc.format.extent 1445846 bytes
dc.format.extent 1445561 bytes
dc.format.mimetype application/pdf
dc.format.mimetype application/pdf
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 en_US
dc.subject Electrical Engineering and Computer Science. en_US
dc.title Understanding expressive action en_US
dc.type Thesis en_US Ph.D. en_US
dc.contributor.department Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. en_US
dc.identifier.oclc 49279861 en_US

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