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Fixation selection for categorical target searches in real world scenes

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TitleInfo
Title
Fixation selection for categorical target searches in real world scenes
Name (type = personal)
NamePart (type = family)
Kleene
NamePart (type = given)
Nicholas
NamePart (type = date)
1988-
DisplayForm
Nicholas Kleene
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Michel
NamePart (type = given)
Melchi
DisplayForm
Melchi Michel
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
chair
Name (type = personal)
NamePart (type = family)
Kowler
NamePart (type = given)
Eileen
DisplayForm
Eileen Kowler
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
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)
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
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
CopyrightDate (encoding = w3cdtf); (qualifier = exact)
2019
Language
LanguageTerm (authority = ISO 639-3:2007); (type = text)
English
Abstract (type = abstract)
Computational models have seen widespread success in predicting fixation locations for visual search tasks that use artificial stimuli, such as Gabors in 1/f noise (Najemnik & Geisler, 2005), but comparatively little in predicting fixation locations for visual search tasks with natural stimuli. Critically, previous approaches have not accounted for the effects of our foveated visual system nor implemented decision rules to actually select a sequence of fixations (Torralba, Oliva, Castelhano, & Henderson, 2006; Ehinger, Hidalgo-Sotelo, Torralba, & Oliva, 2009). Here we present a Bayesian model of fixation selection in visual search tasks using natural images. The model used two known sources of information to select fixations: scene context and features that look similar to the target (termed target-relevant features here). Scene context functioned as a prior over possible target locations, while target-relevant features acted as the likelihood function (as in Ehinger et al., 2009). Scene context was measured using GIST (Torralba et al., 2006) and target-relevant features were measured using Histograms of Oriented Gradients (Dalal & Triggs, 2005). We represented scene context with a mixture of Gaussians and target-relevant features with a multivariate Gaussian distribution. The model selected new fixations using either a maximum a posteriori (MAP) or entropy limit minimization (ELM) rule. To compare the fixations selected by the models we tested human observers on a pedestrian search task in natural images. Prior to the search task, a visibility map was measured using data from human observers in detection task. The visibility map was then used to degrade the target-relevant feature information in our model simulations, representing the effects of foveation. We found evidence that human observers do use scene context and target-relevant features as sources of information to guide their fixations in natural scenes. Additionally, fixations selected by human observers were more consistent with the ELM decision rule than the MAP decision rule. We close by noting the limitations of the models and discuss potential extensions.
Subject (authority = RUETD)
Topic
Psychology
Subject (authority = LCSH)
Topic
Eye tracking
Subject (authority = local)
Topic
Bayesian model
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_10252
PhysicalDescription
Form (authority = gmd)
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (xii, 100 pages) : illustrations
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
RelatedItem (type = host)
TitleInfo
Title
School of Graduate Studies Electronic Theses and Dissertations
Identifier (type = local)
rucore10001600001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier (type = doi)
doi:10.7282/t3-pw4n-2t53
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
Kleene
GivenName
Nicholas
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2019-09-16 13:56:55
AssociatedEntity
Name
Nicholas Kleene
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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DateCreated (point = end); (encoding = w3cdtf); (qualifier = exact)
2019-09-16T10:56:01
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2019-09-27T00:39:10
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