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Object recognition with pictorial structures

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dc.contributor.advisor W. Eric L. Grimson. en_US
dc.contributor.author Felzenszwalb, Pedro F., 1976- en_US
dc.contributor.other Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. en_US
dc.date.accessioned 2005-08-23T21:24:35Z
dc.date.available 2005-08-23T21:24:35Z
dc.date.copyright 2001 en_US
dc.date.issued 2001 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/8576
dc.description Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001. en_US
dc.description Includes bibliographical references (p. 51-53). en_US
dc.description.abstract This thesis presents a statistical framework for object recognition. The framework is motivated by the pictorial structure models introduced by Fischler and Elschlager nearly 30 years ago. The basic idea is to model an object by a collection of parts arranged in a deformable configuration. The appearance of each part is modeled separately, and the deformable configuration is represented by spring-like connections between pairs of parts. These models allow for qualitative descriptions of visual appearance, and are suitable for generic recognition problems. The problem of detecting an object in an image and the problem of learning an object model using training examples are naturally formulated under a statistical approach. We present efficient algorithms to solve these problems in our framework. We demonstrate our techniques by training models to represent faces and human bodies. The models are then used to locate the corresponding objects in novel images. en_US
dc.description.statementofresponsibility by Pedro F. Felzenszwalb. en_US
dc.format.extent 53 p. en_US
dc.format.extent 8399082 bytes
dc.format.extent 8398838 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 http://dspace.mit.edu/handle/1721.1/7582
dc.subject Electrical Engineering and Computer Science. en_US
dc.title Object recognition with pictorial structures en_US
dc.type Thesis en_US
dc.description.degree S.M. en_US
dc.contributor.department Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. en_US
dc.identifier.oclc 49223516 en_US


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