Staff View
Bayesian mixture estimation for perceptual grouping

Descriptive

TitleInfo
Title
Bayesian mixture estimation for perceptual grouping
Name (type = personal)
NamePart (type = family)
Froyen
NamePart (type = given)
Vicky
NamePart (type = date)
1987-
DisplayForm
Vicky Froyen
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Feldman
NamePart (type = given)
Jacob
DisplayForm
Jacob Feldman
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
chair
Name (type = personal)
NamePart (type = family)
Singh
NamePart (type = given)
Manish
DisplayForm
Manish Singh
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
co-chair
Name (type = personal)
NamePart (type = family)
Michel
NamePart (type = given)
Melchi M
DisplayForm
Melchi M Michel
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Zucker
NamePart (type = given)
Steven W
DisplayForm
Steven W Zucker
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
outside member
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
Graduate School - New Brunswick
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (qualifier = exact)
2014
DateOther (qualifier = exact); (type = degree)
2014-01
Place
PlaceTerm (type = code)
xx
Language
LanguageTerm (authority = ISO639-2b); (type = code)
eng
Abstract (type = abstract)
Perceptual grouping is the process by which a set of image elements is divided into distinct “objects” or components. In this dissertation I propose a Bayesian framework for understanding perceptual grouping, in which the goal of the computation is to estimate the organization that best explains the observed configuration of image elements. I formalize the problem of perceptual grouping as a mixture estimation problem, where it is assumed that the configuration of elements is generated by a set of distinct components (or ”objects”), whose underlying parameters one seeks to estimate. In the first part of this dissertation I will propose a simplified version of the framework and show how it can be used to estimate the number of objects, more specifically clusters of dots, present in the image. Across two experiments I show how the model gives an accurate and quantitatively precise account of subjects’ numerosity judgments, while at the same time outperforming more standard accounts for dot clustering. In the second part of the dissertation this simplified framework is expanded to estimate a hierarchical representation of the image elements. This framework can easily be adjusted to different subproblems of perceptual grouping. Here I will show how an instantiation of our framework for contour integration, part decomposition, and shape completion can account for several key perceptual phenomena and previously collected human subject data.
Subject (authority = RUETD)
Topic
Psychology
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_5172
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
xiii, 69 p. : ill.
Note (type = degree)
Ph. D.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by Vicky Froyen
Subject (authority = ETD-LCSH)
Topic
Visual perception
RelatedItem (type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier (type = local)
rucore19991600001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier (type = doi)
doi:10.7282/T3F769N2
Back to the top

Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Froyen
GivenName
Vicky
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2013-11-19 15:34:01
AssociatedEntity
Name
Vicky Froyen
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.
RightsEvent
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2014-01-31
DateTime (encoding = w3cdtf); (qualifier = exact); (point = end)
2014-08-02
Type
Embargo
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after August 2nd, 2014.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
Back to the top

Technical

RULTechMD (ID = TECHNICAL1)
ContentModel
ETD
OperatingSystem (VERSION = 5.1)
windows xp
Back to the top
Version 8.3.2
Rutgers University Libraries - Copyright ©2017