2026-09-18T22:17:46Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/333412022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Trevor J. Darrell.
Ross, Benjamin Charles
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2006-07-13T15:16:42Z
2006-07-13T15:16:42Z
2004
2004
http://hdl.handle.net/1721.1/33341
62394676
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.
Includes bibliographical references (p. 109-110).
In this thesis, I explore the realm of feature-based object. recognition applied to landmark detection. I have built a system using SIFT object recognition and Locality-Sensitive Hashing to quickly and accurately detect landmarks with accuracies ranging from 85-95%. I have also compared PCA-SIFT, a newly developed feature descriptor, to SIFT, and have found that SIFT outperforms it only particular data set. In addition, I have, performed a relatively extensive empirical comparison between Locality-Sensitive Hashing and Best-Bin First, two approximate nearest neighbor searches, finding that Locality-Sensitive Hashing in general performs the best.
by Benjamin Charles Ross.
M.Eng.
110 p.
4941895 bytes
4946485 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.
An analysis of SIFT object recognition with an emphasis on landmark detection
Thesis
application/pdf
false
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="20859ff7-5b9f-4f70-ad1a-cca4d5142584">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Language>eng</Language>
<Title>An analysis of SIFT object recognition with an emphasis on landmark detection</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2004</PublicationDate>
<Authors>
<Author>
<DisplayName>Ross, Benjamin Charles</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 explore the realm of feature-based object. recognition applied to landmark detection. I have built a system using SIFT object recognition and Locality-Sensitive Hashing to quickly and accurately detect landmarks with accuracies ranging from 85-95%. I have also compared PCA-SIFT, a newly developed feature descriptor, to SIFT, and have found that SIFT outperforms it only particular data set. In addition, I have, performed a relatively extensive empirical comparison between Locality-Sensitive Hashing and Best-Bin First, two approximate nearest neighbor searches, finding that Locality-Sensitive Hashing in general performs the best.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>