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Recognition by functional parts

Descriptive

Language
LanguageTerm (authority = ISO 639-3:2007); (type = text)
English
Genre (authority = RULIB-FS)
Other
Genre (authority = marcgt)
technical report
PhysicalDescription
InternetMediaType
application/pdf
Extent
1 online resource (24 pages) : illustrations
Note (type = special display note)
Technical report lcsr-tr-246
Name (authority = RutgersOrg-School); (type = corporate)
NamePart
School of Arts and Sciences (SAS) (New Brunswick)
Name (authority = RutgersOrg-Department); (type = corporate)
NamePart
Computer Science (New Brunswick)
TypeOfResource
Text
Name (type = personal)
NamePart (type = family)
Dickinson
NamePart (type = given)
Sven J.
Affiliation
Computer Science (New Brunswick)
Role
RoleTerm (authority = marcrt); (type = text)
author
Name (type = personal)
NamePart (type = family)
Rosenfeld
NamePart (type = given)
Azriel
Affiliation
University of Maryland
Role
RoleTerm (authority = marcrt); (type = text)
author
Name (type = personal)
NamePart (type = family)
Rivlin
NamePart (type = given)
Ehud
Affiliation
Technion{Israel Institute of Technology
Role
RoleTerm (authority = marcrt); (type = text)
author
TitleInfo
Title
Recognition by functional parts
Abstract (type = abstract)
We present an approach to function-based object recognition that reasons about the functionality of an object's intuitive parts. We extend the popular ``recognition by parts'' shape recognition framework to support ``recognition by functional parts'', by combining a set of functional primitives and their relations with a set of abstract volumetric shape primitives and their relations. Previous approaches have relied on more global object features, often ignoring the problem of object segmentation and thereby restricting themselves to range images of unoccluded scenes. We show how these shape primitives and relations can be easily recovered from superquadric ellipsoids which, in turn, can be recovered from either range or intensity images of occluded scenes. Furthermore, the proposed framework supports both unexpected (bottom-up) object recognition and expected (top-down) object recognition. We demonstrate the approach on a simple domain by recognizing a restricted class of hand-tools from 2-D images.
OriginInfo
DateCreated (encoding = w3cdtf); (keyDate = yes); (qualifier = exact)
1995-06
RelatedItem (type = host)
TitleInfo
Title
Computer Science (New Brunswick)
Identifier (type = local)
rucore21032500001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier (type = doi)
doi:10.7282/t3-xq5q-0g40
Genre (authority = ExL-Esploro)
Technical Documentation
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Rights

RightsDeclaration (AUTHORITY = rightsstatements.org); (TYPE = IN COPYRIGHT); (ID = http://rightsstatements.org/vocab/InC/1.0/)
This Item is protected by copyright and/or related rights.You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use.For other uses you need to obtain permission from the rights-holder(s).
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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Technical

RULTechMD (ID = TECHNICAL1)
ContentModel
Document
CreatingApplication
Version
1.4
ApplicationName
GPL Ghostscript 9.07
DateCreated (point = start); (encoding = w3cdtf); (qualifier = exact)
2018-06-06T12:37:16
DateCreated (point = start); (encoding = w3cdtf); (qualifier = exact)
2018-06-06T12:37:16
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