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Learning on Riemannian manifolds for interpretation of visual environments

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TypeOfResource
Text
TitleInfo (ID = T-1)
Title
Learning on Riemannian manifolds for interpretation of visual environments
Identifier
ETD_1158
Identifier (type = hdl)
http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000050463
Language
LanguageTerm (authority = ISO639-2); (type = code)
eng
Genre (authority = marcgt)
theses
Subject (ID = SBJ-1); (authority = RUETD)
Topic
Computer Science
Subject (ID = SBJ-1); (authority = ETD-LCSH)
Topic
Computer vision
Subject (ID = SBJ-1); (authority = ETD-LCSH)
Topic
Riemannian manifolds
Abstract
Classical machine learning techniques provide effective methods for analyzing data when the parameters of the underlying process lie in a Euclidean space. However, various parameter spaces commonly occurring in computer vision problems violate this assumption. We derive novel learning methods for parameter spaces having Riemannian manifold structure and present several practical applications for scene analysis.
The mean shift algorithm on Lie groups is a generalization of the mean shift procedure which is also an unsupervised learning technique for vector spaces. The derived procedure can be used to cluster data points which form a matrix Lie group. We present an application of the new algorithm for multiple 3D rigid motion estimation problem from noisy point correspondences in the presence of outliers. The approach performs simultaneous estimation of all the motions and does not require prior specification of the number of motion groups.
We present a novel geometric framework to learn a supervised classification model for data points lying on a connected Riemannian manifold. The structure of the classifier is an additive model, where the weak learners are trained on the tangent spaces of the manifold. The derived algorithm is applied to pedestrian detection problem which is known to be among the hardest examples of the detection tasks.
We describe a regression model where the response parameters form a Lie group. The model is utilized for affine tracking problem where the motion is estimated as a parameter of the image observations. We present generalization of the learning model to build an invariant object detector from an existing pose dependent detector. The proposed model can accurately detect objects in various poses, where the size of the search space is only a fraction compared to the existing detection methods.
The other contributions of the thesis include a novel region descriptor and an online learning algorithm for estimating background statistics of a scene which are utilized for several challenging problems such as matching, tracking, texture classification and low frame rate tracking.
PhysicalDescription
Extent
xix, 156 p. : ill.
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Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references (p. 143-154)
Note (type = statement of responsibility)
by Cuneyt Oncel Tuzel
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Tuzel
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Cuneyt Oncel
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Cuneyt Oncel Tuzel
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Meer
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Peter
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Advisory Committee
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Peter Meer
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Kulikowski
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Casimir
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internal member
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Advisory Committee
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Casimir Kulikowski
Name (ID = NAME-4); (type = personal)
NamePart (type = family)
DeCarlo
NamePart (type = given)
Douglas
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internal member
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Advisory Committee
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Douglas DeCarlo
Name (ID = NAME-5); (type = personal)
NamePart (type = family)
Matei
NamePart (type = given)
Bogdan
Role
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outside member
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Advisory Committee
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Bogdan Matei
Name (ID = NAME-1); (type = corporate)
NamePart
Rutgers University
Role
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degree grantor
Name (ID = NAME-2); (type = corporate)
NamePart
Graduate School - New Brunswick
Role
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school
OriginInfo
DateCreated (point = ); (qualifier = exact)
2008
DateOther (qualifier = exact); (type = degree)
2008-10
Place
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xx
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TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
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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/T3P55NTJ
Genre (authority = ExL-Esploro)
ETD doctoral
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The author owns the copyright to this work.
Copyright
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Copyright protected
Availability
Status
Open
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Detail
Non-exclusive ETD license
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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.
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