Approximate inference methods for grid-structured MRFs
Name
56821432-MIT.pdf
Description
Full printable version
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2.11 MB
Format
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Checksum (MD5)
7b2ce8b531b2e6ab99732929d36faef1
Author(s)
Battocchi, Keith, 1980-
Advisor(s)
Leslie Kaelbling.
Alternative Title
Approximate inference methods for grid-structured Markov Random Field
Date Issued
2004
Publisher
Massachusetts Institute of Technology
Abstract
In this thesis, I compared the mean field, belief propagation, and graph cuts methods for performing approximate inference on an MRF. I developed a method by which the memory requirements for belief propagation could be significantly reduced. I also developed a modification of the graph cuts algorithm that allows it to work on MRFs with very general potential functions. These changes make it possible to use any of the three algorithms on medical imaging problems. The three algorithms were then tested on simulated problems so that their accuracy and efficiency could be compared.
Description
Thesis (M. Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.
Includes bibliographical references (p. 43-44).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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