Probabilistic geometric grammars for object recognition
Name
70124561-MIT.pdf
Description
Full printable version
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9.78 MB
Format
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Author(s)
Aycinena, Margaret Aida
Advisor(s)
Leslie Pack Kaelbling and Tomás Lozano-Pérez.
Date Issued
2005
Publisher
Massachusetts Institute of Technology
Abstract
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.
Description
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.
Includes bibliographical references (p. 121-123).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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