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Inference of 3D shape from line drawings

Descriptive

TitleInfo
Title
Inference of 3D shape from line drawings
Name (type = personal)
NamePart (type = family)
Kim
NamePart (type = given)
Seha
NamePart (type = date)
1976-
DisplayForm
Seha Kim
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)
internal member
Name (type = personal)
NamePart (type = family)
Papathomas
NamePart (type = given)
Thomas
DisplayForm
Thomas Papathomas
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Elgammal
NamePart (type = given)
Ahmed
DisplayForm
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
Graduate School - New Brunswick
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (encoding = w3cdtf); (qualifier = exact)
2015
DateOther (qualifier = exact); (type = degree)
2015-10
CopyrightDate (encoding = w3cdtf); (qualifier = exact)
2015
Place
PlaceTerm (type = code)
xx
Language
LanguageTerm (authority = ISO639-2b); (type = code)
eng
Abstract (type = abstract)
Line drawings lack direct 3D depth information, yet human vision easily perceives the 3D shapes from the contours. This dissertation investigates the mechanisms underlying the 3D shape inference from 2D line drawings. Here, four psychophysical experiments and a computational model for the 3D shape inference are discussed. Experiment 1 shows that human responses in depth judgments for line drawings reflect an underlying uncertainty of the perceived 3D shape, which is based on the complex interaction of local and global depth cues propagated from the contours. The computational model estimates the posterior probability of possible 3D surfaces from the contours of a line drawing in a Bayesian framework. The comparison of the model predictions and human depth responses for the line drawings from Experiment 1 demonstrates that the model accounts for the probabilistic 3D shape interpretation of line drawings by human vision. Experiment 2 shows that the reliability of a contour segment in a line drawing as a meaningful depth cue is conditional to the complex global context. Experiments 3 and 4 show that the certainty of depth difference perceptions from partial line drawings increases as more non-local visual cues are available. The experiments and the model offer a new perspective on 3D shape perception from line drawings as an inference based on the probability over possible 3D shapes given the contour cues, providing a broader understanding on the mechanisms of human vision.
Subject (authority = RUETD)
Topic
Psychology
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_6549
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (ix, 58 p. : ill.)
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Subject (authority = ETD-LCSH)
Topic
Visual perception
Subject (authority = ETD-LCSH)
Topic
Shapes
Note (type = statement of responsibility)
by Seha Kim
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/T3G162TN
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
Kim
GivenName
Seha
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2015-05-11 11:12:34
AssociatedEntity
Name
Seha Kim
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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Technical

RULTechMD (ID = TECHNICAL1)
ContentModel
ETD
OperatingSystem (VERSION = 5.1)
windows xp
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