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The influence of complexity on the detection of contours

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TitleInfo
Title
The influence of complexity on the detection of contours
Name (type = personal)
NamePart (type = family)
Wilder
NamePart (type = given)
John
NamePart (type = date)
1984-
DisplayForm
John Wilder
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Feldman
NamePart (type = given)
Jacob
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Jacob Feldman
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
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)
Kowler
NamePart (type = given)
Eileen
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Eileen Kowler
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 (qualifier = exact)
2013
DateOther (qualifier = exact); (type = degree)
2013-10
Place
PlaceTerm (type = code)
xx
Language
LanguageTerm (authority = ISO639-2b); (type = code)
eng
Abstract (type = abstract)
Detecting objects in visual scenes is an important function of the visual system. Studies of contour detection and contour integration have answered many questions about the human visual system, but the role of contour geometry is not well understood. This thesis considers the problem of contour detection as a Bayesian decision problem. I begin by describing a generative model for natural contours. Bayesian arguments predict that simple contours (high probability under the generative model) should be easier to detect than more complex (lower probability) ones. In the case of open contours (Experiments 1 and 2), a complexity measure follows from a well-established contour-generating model. For closed contours, which have been far less studied, complexity measures require a more novel model that involves the shape of the region enclosed by the contour. The results of closed contours (Experiments 3 and 4) show that the complexity of the contour and the complexity of the shape of the bounded region jointly affect the ability of human observers to detect the contour in a noise field. In summary, contour integration has been treated mainly as a local grouping problem, but these results suggest that there is an important role for global factors in detection. Additionally, while the mathematical framework for measuring complexity was used here to study contour detection, it is also general enough to be useful in all areas of pattern detection where an explicit generative model is defined.
Subject (authority = RUETD)
Topic
Psychology
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_4932
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
viii, 85 p. : ill.
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by John Wilder
Subject (authority = ETD-LCSH)
Topic
Visual perception
Subject (authority = ETD-LCSH)
Topic
Bayesian statistical decision theory
Subject (authority = ETD-LCSH)
Topic
Curves
Subject (authority = ETD-LCSH)
Topic
Shapes
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/T3H9937N
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
Wilder
GivenName
John
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2013-08-02 16:23:59
AssociatedEntity
Name
John Wilder
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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