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Multi-linear algebra based techniques for foreground and background separation

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TitleInfo
Title
Multi-linear algebra based techniques for foreground and background separation
Name (type = personal)
NamePart (type = family)
Tadimeti
NamePart (type = given)
Neha
NamePart (type = date)
1992-
DisplayForm
Neha Tadimeti
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Bajwa
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Waheed U.
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Waheed U. Bajwa
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Advisory Committee
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chair
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NamePart (type = family)
Torres
NamePart (type = given)
Elizabeth
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Elizabeth Torres
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Advisory Committee
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internal member
Name (type = personal)
NamePart (type = family)
Gajic
NamePart (type = given)
Zoran
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Zoran Gajic
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Advisory Committee
Role
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internal member
Name (type = personal)
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Patel
NamePart (type = given)
Vishal M.
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Vishal M. Patel
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Advisory Committee
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internal member
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Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
Graduate School - New Brunswick
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school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (qualifier = exact)
2017
DateOther (qualifier = exact); (type = degree)
2017-01
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2017
Place
PlaceTerm (type = code)
xx
Language
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eng
Abstract (type = abstract)
The work presented in this thesis aims to understand the use of tensor algebra for background and foreground separation in videos. Specifically, it tries to explore the advantages of tensor-based approaches over the vector-based ones. In vector-based approaches, video frames are vectorized and concatenated into columns of a matrix for foreground and background separation. Through vectorization, one cannot explore the multi-dimensional aspect of video frames. Recent research has shown that tensor algebra can be helpful in extracting useful information from a multi-dimensional perspective. In this thesis, we propose two new algorithms which use tensor algebra to solve for background and foreground separation. In the first part of the thesis, we develop a mini-batch extension to Online Tensor Robust Principal Component Analysis (OTRPCA). The proposed extension signifi- cantly reduces the computational time in comparison to OTRPCA. It is also shown that the accuracy levels of background separation are higher than OTRPCA for a decent mini-batch size. As the mini-batch size further increases, accuracy levels fall as the dictionary update is one-shot and non-iterative. In the second part of the thesis, online vector-based Grassmanian Robust Adaptive Subspace Algorithm (GRASTA) is extended to tensor domain. The proposed Multi-Linear GRASTA (MLG) is also an online algorithm, thus suitable for real-time applications. Unlike the vector-based implementation, MLG explores the multi-dimensional nature of the video frames and solves for the separation problem across every dimension. MLG can process multiple frames at a time making it faster than other vector and tensor based separation algorithms. Detailed results are discussed which show that the accuracy of separation with MLG is competitive with the state-of-the-art.
Subject (authority = RUETD)
Topic
Electrical and Computer Engineering
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_7838
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (vii, 26 p. : ill.)
Note (type = degree)
M.S.
Note (type = bibliography)
Includes bibliographical references
Subject (authority = ETD-LCSH)
Topic
Tensor algebra
Subject (authority = ETD-LCSH)
Topic
Image processing
Note (type = statement of responsibility)
by Beha Tadimeti
RelatedItem (type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier (type = local)
rucore19991600001
Location
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NjNbRU
Identifier (type = doi)
doi:10.7282/T3XD145W
Genre (authority = ExL-Esploro)
ETD graduate
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Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Tadimeti
GivenName
Neha
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2017-01-09 16:27:31
AssociatedEntity
Name
Neha Tadimeti
Role
Copyright holder
Affiliation
Rutgers University. Graduate School - New Brunswick
AssociatedObject
Type
License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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2017-01-09T03:57:56
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