Staff View
Matching and recognition of shapes using chord-based point density graphs

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 (9 pages)
Note (type = special display note)
Technical report DCS-TR-566
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
TitleInfo
Title
Matching and recognition of shapes using chord-based point density graphs
Abstract (type = abstract)
We present a novel approach to matching and recognition of 2D shapes, which uses a chord-based shape representation and matches shapes by finding correspondences between chords. Given a 2D shape, we choose its basis chord such that both end points of the chord have high probability of being present under occlusion. Then the shape is transformed and represented in an internal reference frame uniquely defined by the basis chord. In this chord-based reference frame, we capture the coordinate distribution of all shape points using a point density graph. When a second shape is being matched against the first shape, we pursue a chord on the second shape based on which the second shape’s point density graph is the closest to that of the first shape. To avoid exhaustive search of all chords on the second shape, we use the Chord Length Distributions of the two shapes to prune a dominant portion of the search space. The distance between two point density graphs is measured using a symmetrized Kullback-Leibler divergence. Then when the correspondence between a pair of chords is established, a unique similarity transform is determined to match the two shapes so that the corresponding chords are aligned. Finally, we employ a hierarchical approach to extend our method to include Affine transformations. Matching and Recognition results from the Brown SIID project shapes, the MNIST dataset of handwritten digits, and the SQUID fish database, demonstrate our algorithm’s performance, its invariance properties and its robustness to occlusion, articulation, missing gaps, and spurious structures on shapes.
Name (type = personal)
NamePart (type = family)
Huang
NamePart (type = given)
Xiaolei
Affiliation
Computer Science (New Brunswick)
Role
RoleTerm (authority = marcrt); (type = text)
author
Name (type = personal)
NamePart (type = family)
Metaxas
NamePart (type = given)
Dimitris
Affiliation
Computer Science (New Brunswick)
Role
RoleTerm (authority = marcrt); (type = text)
author
OriginInfo
DateCreated (encoding = w3cdtf); (keyDate = yes); (qualifier = exact)
2002
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/t3-0hjf-av09
Genre (authority = ExL-Esploro)
Technical Documentation
Back to the top

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
Back to the top

Technical

RULTechMD (ID = TECHNICAL1)
ContentModel
Document
CreatingApplication
Version
1.4
ApplicationName
GPL Ghostscript 9.07
DateCreated (point = start); (encoding = w3cdtf); (qualifier = exact)
2018-06-06T12:34:31
DateCreated (point = start); (encoding = w3cdtf); (qualifier = exact)
2018-06-06T12:34:31
Back to the top
Version 8.3.13
Rutgers University Libraries - Copyright ©2020