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Robust methods for multiple model discovery in structured and unstructured data

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TitleInfo
Title
Robust methods for multiple model discovery in structured and unstructured data
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Anand
NamePart (type = given)
Saket
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Saket Anand
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author
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Peter
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Peter Meer
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Advisory Committee
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chair
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LAWRENCE
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internal member
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Athina
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Athina Petropulu
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Advisory Committee
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internal member
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Dana
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Kristin
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Kristin Dana
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Advisory Committee
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internal member
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NamePart (type = family)
Singh
NamePart (type = given)
Maneesh
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Maneesh Singh
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Advisory Committee
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outside member
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Rutgers University
Role
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degree grantor
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Graduate School - New Brunswick
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Text
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theses
OriginInfo
DateCreated (qualifier = exact)
2013
DateOther (qualifier = exact); (type = degree)
2013-10
Place
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xx
Language
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eng
Abstract (type = abstract)
Humans excel at identifying and locating multiple instances of objects or persons in a scene, despite large variations in lighting conditions, pose or scale. In order to achieve this level of robustness, humans naturally use high-level semantic information. The notion of semantics is hierarchical in nature, for example, a house is constituted of walls, floor and ceiling. When certain entities in this hierarchy can be modeled using a functional form known a priori, we refer to the data as structured, while when the functional form is unknown, the data is unstructured. In this thesis, we address the problem of discovering multiple instances of models in structured and unstructured data. The first part of this thesis deals with structured data. We identify planar regions in an indoor scene by using a single depth image from a Microsoft Kinect sensor. The clutter in the indoor scenes, the depth dependent measurement noise and the unknown number of planar regions pose serious challenges in model discovery. We propose a scalable bottom-up approach that leverages from a heteroscedastic, i.e., point dependent model of the measurement noise. The second part of the thesis addresses multiple model discovery in unstructured data in a semi-supervised setup. We develop a framework for using mean shift clustering in kernel spaces with a few user-specified pairwise constraints. A linear transformation of the initial kernel space is learned by the constrained minimization of a Bregman divergence based objective function. We automatically determine the adaptive bandwidth parameter to be used with mean shift clustering. Finally, we compare the performance with state-of-the-art semi-supervised clustering methods and show that kernel mean shift clustering performs particularly well when the number of clusters is large. We also propose a few directions for future research. Using the planar regions detected from the first frame of Kinect, a sequence of RGB-D images can be rapidly processed to dynamically generate a consistent 3D model of the scene. We also show that for the kernel learning problem, we can use ideas from group theory and semi-definite programming to devise a more efficient algorithm that only uses linearly independent constraints.
Subject (authority = RUETD)
Topic
Electrical and Computer Engineering
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Title
Rutgers University Electronic Theses and Dissertations
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ETD
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ETD_4905
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electronic resource
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application/pdf
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text/xml
Extent
xiv, 107 p. : ill.
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Note (type = vita)
Includes vita
Note (type = statement of responsibility)
by Saket Anand
Subject (authority = ETD-LCSH)
Topic
Semantics--Data processing
Subject (authority = ETD-LCSH)
Topic
Three-dimensional imaging
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/T3VQ30NR
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

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The author owns the copyright to this work.
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Name
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Anand
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Saket
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Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2013-07-10 09:59:09
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Name
Saket Anand
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Affiliation
Rutgers University. Graduate School - New Brunswick
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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.
RightsEvent
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2013-10-31
DateTime (encoding = w3cdtf); (qualifier = exact); (point = end)
2014-10-31
Type
Embargo
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after October 31st, 2014.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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windows xp
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