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Skeletal shape similarity and shape classification

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TitleInfo
Title
Skeletal shape similarity and shape classification
Name (type = personal)
NamePart (type = family)
Destler
NamePart (type = given)
Nathan
NamePart (type = date)
1989-
DisplayForm
Nathan Destler
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Feldman
NamePart (type = given)
Jacob
DisplayForm
Jacob Feldman
Affiliation
Advisory Committee
Role
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chair
Name (type = personal)
NamePart (type = family)
Singh
NamePart (type = given)
Manish
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Manish Singh
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Michel
NamePart (type = given)
Melchi
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Melchi Michel
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Elgammal
NamePart (type = given)
Ahmed
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Ahmed Elgammal
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
School of Graduate Studies
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (encoding = w3cdtf); (keyDate = yes); (qualifier = exact)
2019
DateOther (encoding = w3cdtf); (qualifier = exact); (type = degree)
2019-10
Language
LanguageTerm (authority = ISO 639-3:2007); (type = text)
English
Abstract (type = abstract)
What makes two shapes similar? Given two shapes, is there a mathematically principled way to predict human similarity judgments, and consequent shape classification judgments? To answer this question, an experimental framework was developed for rapidly collecting human judgments of whether shapes did or did not belong to novel shape categories. The subjects’ judgments of category membership were compared against the predictions of several different shape similarity/classification models. Among these models, I propose a new lattice similarity model of shape similarity based on Bayesian shape skeletons. An earlier model of similarity based on the same Bayesian shape skeletons, the cross likelihood, has been shown to be an effective predictor of human shape discrimination, but this model applies only to shapes with similar part structures. The lattice similarity model is more principled and general, and is suitable for comparing arbitrary shape pairs in both 2D and 3D domains. This new model provides a better overall fit to human data than a number of competing models, including the cross likelihood model, an out-of-the-box convolutional neural network model, and a non-skeletal part-based similarity model proposed by Erdogan and Jacobs (2017). The lattice similarity model predicted human data more accurately than all other tested models in experiments that used 3D shapes, and most other models in experiments that used 2D shapes. Prototype-like and exemplar-like versions of the lattice similarity model were also compared using the same human data as above; the prototype-like version fits the experimental data better than the exemplar-like version.
Subject (authority = RUETD)
Topic
Psychology
Subject (authority = local)
Topic
Vision
Subject (authority = LCSH)
Topic
Visual perception
Subject (authority = LCSH)
Topic
Shapes
Subject (authority = LCSH)
Topic
Similarity (Psychology)
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_10310
PhysicalDescription
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application/pdf
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text/xml
Extent
1 online resource (vi, 64 pages) : illustrations
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
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TitleInfo
Title
School of Graduate Studies Electronic Theses and Dissertations
Identifier (type = local)
rucore10001600001
Location
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NjNbRU
Identifier (type = doi)
doi:10.7282/t3-e07g-xs56
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Destler
GivenName
Nathan
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2019-09-24 00:10:56
AssociatedEntity
Name
Nathan Destler
Role
Copyright holder
Affiliation
Rutgers University. School of Graduate Studies
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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Technical

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windows xp
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1.5
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2019-09-16T13:20:59
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2019-09-16T13:20:59
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