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Shape skeletons and shape similarity
Descriptive
TypeOfResource
Text
TitleInfo
(ID = T-1)
Title
Shape skeletons and shape similarity
Identifier
(type = hdl)
http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.17444
Identifier
ETD_1184
Language
LanguageTerm
(authority = ISO 639-3:2007)
English
Genre
(authority = marcgt)
theses
Subject
(ID = SBJ-1);
(authority = RUETD)
Topic
Psychology
Subject
(ID = SBJ-2);
(authority = ETD-LCSH)
Topic
Cognitive psychology
Subject
(ID = SBJ-3);
(authority = ETD-LCSH)
Topic
Cognition
Abstract
Judgments of similarity play an integral role in the human cognitive system as they provide a means for extracting information about how objects in the world relate to each other. This similarity information is applied in various cognitive tasks, such as categorization, recognition, and identification. Previous work suggests that perceived objects are cognitively represented in a psychological space where similarity is preserved, allowing for an internal structured representation of objects in the world (Shepard, 1964). For an internal representation to be formed, information about an object must be extracted. Shape, a highly informative and salient property of an object, is often used. Judgments made about shape similarity reflect how humans functionally represent and utilize shape information from an object. Computational shape representation has been achieved with varying amounts of success (e.g. Blum, 1973; Biederman, 1987). This variability is due, in part, to the complexity of mimicking the seemingly effortless human ability to make judgments about shape even in spite of numerous possible complications, such as sparse information and occlusions. This work presents the use of a Bayesian estimation of a shape's skeleton, the maximum a posteriori (MAP) skeleton (Feldman & Singh, 2006), as part of a generative model of shape that allows for the computation of a probabilistically-based similarity metric. This method of shape representation makes possible the prediction of similarity judgments reported by human subjects on collections of shapes that exhibit differences in both part structure and metric qualities and that have been generated by an unrelated process. It is argued that the derivation of a similarity metric from this model provides the previously unavailable relationship between shape representation and categorical judgments about shape.
PhysicalDescription
Extent
x, 92 pages
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application/pdf
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text/xml
Note
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Ph.D.
Note
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Includes bibliographical references (p. 83-91).
Name
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NamePart
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Briscoe
NamePart
(type = given)
Erica Jan
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author
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Erica Jan Briscoe
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(type = family)
Feldman
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(type = given)
Jacob
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chair
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Advisory Committee
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Jacob Feldman
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(ID = NAME-3);
(type = personal)
NamePart
(type = family)
Kowler
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(type = given)
Eileen
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(authority = RULIB)
internal member
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Advisory Committee
DisplayForm
Eileen Kowler
Name
(ID = NAME-4);
(type = personal)
NamePart
(type = family)
Singh
NamePart
(type = given)
Manish
Role
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(authority = RULIB)
internal member
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Advisory Committee
DisplayForm
Manish Singh
Name
(ID = NAME-5);
(type = personal)
NamePart
(type = family)
Elgammal
NamePart
(type = given)
Ahmed
Role
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(authority = RULIB)
outside member
Affiliation
Advisory Committee
DisplayForm
Ahmed Elgammal
Name
(ID = NAME-1);
(type = corporate)
NamePart
Rutgers University
Role
RoleTerm
(authority = RULIB)
degree grantor
Name
(ID = NAME-2);
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NamePart
Graduate School - New Brunswick
Role
RoleTerm
(authority = RULIB)
school
OriginInfo
DateCreated
(qualifier = exact)
2008
DateOther
(qualifier = exact);
(type = degree)
2008-10
Location
PhysicalLocation
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NjNbRU
RelatedItem
(type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier
(type = local)
rucore19991600001
Identifier
(type = doi)
doi:10.7282/T3C24WSS
Genre
(authority = ExL-Esploro)
ETD doctoral
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Rights
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The author owns the copyright to this work.
Copyright
Status
Copyright protected
Availability
Status
Open
AssociatedEntity
(AUTHORITY = rulib);
(ID = 1)
Name
Erica Briscoe
Role
Copyright holder
Affiliation
Rutgers University. Graduate School - New Brunswick
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Permission or license
Detail
Non-exclusive ETD license
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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.
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Technical
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application/x-tar
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754688
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application/x-tar
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