2026-09-18T21:23:05Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/768122022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Christopher Yu and Piotr Indyk.
Gardiner, Brian Calvin
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2013-02-13T21:23:30Z
2013-02-13T21:23:30Z
2012
2012
http://hdl.handle.net/1721.1/76812
824151491
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (p. 61-62).
We explore the utility of a dimensionality reducing process we term folding for the purposes of image feature extraction. We seek to discover whether image features are preserved under this process and how to efficiently extract them. The application is in size weight and power constrained imaging scenarios where an efficient implementation of this dimensionality reduction can save power and computation costs. The specific features we explore are image corners, rotation, and translation. We present algorithms for recovering these features from folded representations of images followed by simulation results showing the performance of the algorithms when operating on real images.
by Brian Calvin Gardiner.
M.Eng.
62 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Compressive image feature extraction by means of folding
Thesis
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Publication
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<Abstract>We explore the utility of a dimensionality reducing process we term folding for the purposes of image feature extraction. We seek to discover whether image features are preserved under this process and how to efficiently extract them. The application is in size weight and power constrained imaging scenarios where an efficient implementation of this dimensionality reduction can save power and computation costs. The specific features we explore are image corners, rotation, and translation. We present algorithms for recovering these features from folded representations of images followed by simulation results showing the performance of the algorithms when operating on real images.</Abstract>
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