2026-09-19T00:37:30Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/270862026-06-06T01:04:31Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Leslie Kaelbling.
Battocchi, Keith, 1980-
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2005-09-06T21:40:17Z
2005-09-06T21:40:17Z
2004
2004
http://hdl.handle.net/1721.1/27086
56821432
Thesis (M. Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.
Includes bibliographical references (p. 43-44).
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.
by Keith Battocchi.
M.Eng.
S.B.
49 p.
1850366 bytes
1853314 bytes
application/pdf
application/pdf
en_US
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.
Approximate inference methods for grid-structured MRFs
Approximate inference methods for grid-structured Markov Random Field
Thesis
application/pdf
false
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="a67ea27a-bd7c-411c-bf67-8eb8d1083ccc">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Language>en_US</Language>
<Title>Approximate inference methods for grid-structured MRFs</Title>
<Subtitle>Approximate inference methods for grid-structured Markov Random Field</Subtitle>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2004</PublicationDate>
<Authors>
<Author>
<DisplayName>Battocchi, Keith, 1980-</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>http://dspace.mit.edu/handle/1721.1/7582</License>
<Keyword>Electrical Engineering and Computer Science.</Keyword>
<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.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>