2026-09-18T22:30:59Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/346402022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Leslie Pack Kaelbling and Tomás Lozano-Pérez.
Aycinena, Margaret Aida
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2006-11-07T16:43:50Z
2006-11-07T16:43:50Z
2005
2005
http://hdl.handle.net/1721.1/34640
70124561
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.
Includes bibliographical references (p. 121-123).
This thesis presents a generative three-dimensional (3D) representation and recognition framework for classes of objects. The framework uses probabilistic grammars to represent object classes recursively in terms of their parts, thereby exploiting the hierarchical and substitutive structure inherent to many types of objects. The framework models the 3) geometric characteristics of object parts using multivariate conditional Gaussians over dimensions, position, and rotation. I present algorithms for learning geometric models and rule probabilities given parsed 3D examples and a fixed grammar. I also present a parsing algorithm for classifying unlabeled, unparsed 3D examples given a geometric grammar. Finally, I describe the results of a set of experiments designed to investigate the chosen model representation of the framework.
by Margaret Aida Aycinena.
S.M.
123 p.
7497597 bytes
7503768 bytes
application/pdf
application/pdf
eng
Massachusetts Institute of Technology
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.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Probabilistic geometric grammars for object recognition
Thesis
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