Ellipse-based Principal Component Analysis for Self-intersecting Curve Reconstruction from Noisy Point Sets

Surface reconstruction from cross cuts usually requires curve reconstruction from planar noisy point samples -- The output curves must form a possibly disconnected 1manifold for the surface reconstruction to proceed -- This article describes an implemented algorithm for the reconstruction of planar...

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Autores:
Ruíz, O.
Vanegas, C.
Cadavid, C.
Tipo de recurso:
Fecha de publicación:
2011
Institución:
Universidad EAFIT
Repositorio:
Repositorio EAFIT
Idioma:
eng
OAI Identifier:
oai:repository.eafit.edu.co:10784/9681
Acceso en línea:
http://hdl.handle.net/10784/9681
Palabra clave:
CURVAS PLANAS
COLECTORES (INGENIERÍA)
TOPOLOGÍA
VARIEDADES (MATEMÁTICAS)
CORRELACIÓN (ESTADÍSTICA)
ANÁLISIS ESTOCÁSTICO
FUNCIONES ELÍPTICAS
Curves, plane
Topology
Manifolds (Mathematics)
Correlation (statistics)
Stochastic analysis
Functions, elliptic
Curves
plane
Topology
Manifolds (Mathematics)
Correlation (statistics)
Stochastic analysis
Functions
elliptic
Reconstrucción superficial
Nube de puntos
Rights
License
Springer-Verlag 2010
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dc.title.eng.fl_str_mv Ellipse-based Principal Component Analysis for Self-intersecting Curve Reconstruction from Noisy Point Sets
title Ellipse-based Principal Component Analysis for Self-intersecting Curve Reconstruction from Noisy Point Sets
spellingShingle Ellipse-based Principal Component Analysis for Self-intersecting Curve Reconstruction from Noisy Point Sets
CURVAS PLANAS
COLECTORES (INGENIERÍA)
TOPOLOGÍA
VARIEDADES (MATEMÁTICAS)
CORRELACIÓN (ESTADÍSTICA)
ANÁLISIS ESTOCÁSTICO
FUNCIONES ELÍPTICAS
Curves, plane
Topology
Manifolds (Mathematics)
Correlation (statistics)
Stochastic analysis
Functions, elliptic
Curves
plane
Topology
Manifolds (Mathematics)
Correlation (statistics)
Stochastic analysis
Functions
elliptic
Reconstrucción superficial
Nube de puntos
title_short Ellipse-based Principal Component Analysis for Self-intersecting Curve Reconstruction from Noisy Point Sets
title_full Ellipse-based Principal Component Analysis for Self-intersecting Curve Reconstruction from Noisy Point Sets
title_fullStr Ellipse-based Principal Component Analysis for Self-intersecting Curve Reconstruction from Noisy Point Sets
title_full_unstemmed Ellipse-based Principal Component Analysis for Self-intersecting Curve Reconstruction from Noisy Point Sets
title_sort Ellipse-based Principal Component Analysis for Self-intersecting Curve Reconstruction from Noisy Point Sets
dc.creator.fl_str_mv Ruíz, O.
Vanegas, C.
Cadavid, C.
dc.contributor.department.spa.fl_str_mv Universidad EAFIT. Departamento de Ingeniería Mecánica
dc.contributor.author.none.fl_str_mv Ruíz, O.
Vanegas, C.
Cadavid, C.
dc.contributor.researchgroup.spa.fl_str_mv Laboratorio CAD/CAM/CAE
dc.subject.lemb.spa.fl_str_mv CURVAS PLANAS
COLECTORES (INGENIERÍA)
TOPOLOGÍA
VARIEDADES (MATEMÁTICAS)
CORRELACIÓN (ESTADÍSTICA)
ANÁLISIS ESTOCÁSTICO
FUNCIONES ELÍPTICAS
topic CURVAS PLANAS
COLECTORES (INGENIERÍA)
TOPOLOGÍA
VARIEDADES (MATEMÁTICAS)
CORRELACIÓN (ESTADÍSTICA)
ANÁLISIS ESTOCÁSTICO
FUNCIONES ELÍPTICAS
Curves, plane
Topology
Manifolds (Mathematics)
Correlation (statistics)
Stochastic analysis
Functions, elliptic
Curves
plane
Topology
Manifolds (Mathematics)
Correlation (statistics)
Stochastic analysis
Functions
elliptic
Reconstrucción superficial
Nube de puntos
dc.subject.keyword.spa.fl_str_mv Curves, plane
Topology
Manifolds (Mathematics)
Correlation (statistics)
Stochastic analysis
Functions, elliptic
dc.subject.keyword.eng.fl_str_mv Curves
plane
Topology
Manifolds (Mathematics)
Correlation (statistics)
Stochastic analysis
Functions
elliptic
dc.subject.keyword..keywor.fl_str_mv Reconstrucción superficial
Nube de puntos
description Surface reconstruction from cross cuts usually requires curve reconstruction from planar noisy point samples -- The output curves must form a possibly disconnected 1manifold for the surface reconstruction to proceed -- This article describes an implemented algorithm for the reconstruction of planar curves (1manifolds) out of noisy point samples of a sel-fintersecting or nearly sel-fintersecting planar curve C -- C:[a,b]⊂R→R is self-intersecting if C(u)=C(v), u≠v, u,v∈(a,b) (C(u) is the self-intersection point) -- We consider only transversal self-intersections, i.e. those for which the tangents of the intersecting branches at the intersection point do not coincide (C′(u)≠C′(v)) -- In the presence of noise, curves which self-intersect cannot be distinguished from curves which nearly sel fintersect -- Existing algorithms for curve reconstruction out of either noisy point samples or pixel data, do not produce a (possibly disconnected) Piecewise Linear 1manifold approaching the whole point sample -- The algorithm implemented in this work uses Principal Component Analysis (PCA) with elliptic support regions near the selfintersections -- The algorithm was successful in recovering contours out of noisy slice samples of a surface, for the Hand, Pelvis and Skull data sets -- As a test for the correctness of the obtained curves in the slice levels, they were input into an algorithm of surface reconstruction, leading to a reconstructed surface which reproduces the topological and geometrical properties of the original object -- The algorithm robustly reacts not only to statistical noncorrelation at the self-intersections(nonmanifold neighborhoods) but also to occasional high noise at the nonselfintersecting (1manifold) neighborhoods
publishDate 2011
dc.date.issued.none.fl_str_mv 2011
dc.date.available.none.fl_str_mv 2016-11-18T22:14:34Z
dc.date.accessioned.none.fl_str_mv 2016-11-18T22:14:34Z
dc.type.eng.fl_str_mv info:eu-repo/semantics/article
article
info:eu-repo/semantics/publishedVersion
publishedVersion
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dc.type.local.spa.fl_str_mv Artículo
status_str publishedVersion
dc.identifier.issn.none.fl_str_mv 1432-2315
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/10784/9681
dc.identifier.doi.none.fl_str_mv 10.1007/s00371-010-0527-x
identifier_str_mv 1432-2315
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url http://hdl.handle.net/10784/9681
dc.language.iso.eng.fl_str_mv eng
language eng
dc.relation.ispartof.spa.fl_str_mv The Visual Computer, Collection: Computer Science, Volume 27, Issue 3, pp. 211-226
dc.relation.uri.none.fl_str_mv http://dx.doi.org/10.1007/s00371-010-0527-x
dc.rights.spa.fl_str_mv Springer-Verlag 2010
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.local.spa.fl_str_mv Acceso abierto
rights_invalid_str_mv Springer-Verlag 2010
Acceso abierto
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dc.publisher.spa.fl_str_mv Springer Berlin Heidelberg
institution Universidad EAFIT
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spelling 2016-11-18T22:14:34Z20112016-11-18T22:14:34Z1432-2315http://hdl.handle.net/10784/968110.1007/s00371-010-0527-xSurface reconstruction from cross cuts usually requires curve reconstruction from planar noisy point samples -- The output curves must form a possibly disconnected 1manifold for the surface reconstruction to proceed -- This article describes an implemented algorithm for the reconstruction of planar curves (1manifolds) out of noisy point samples of a sel-fintersecting or nearly sel-fintersecting planar curve C -- C:[a,b]⊂R→R is self-intersecting if C(u)=C(v), u≠v, u,v∈(a,b) (C(u) is the self-intersection point) -- We consider only transversal self-intersections, i.e. those for which the tangents of the intersecting branches at the intersection point do not coincide (C′(u)≠C′(v)) -- In the presence of noise, curves which self-intersect cannot be distinguished from curves which nearly sel fintersect -- Existing algorithms for curve reconstruction out of either noisy point samples or pixel data, do not produce a (possibly disconnected) Piecewise Linear 1manifold approaching the whole point sample -- The algorithm implemented in this work uses Principal Component Analysis (PCA) with elliptic support regions near the selfintersections -- The algorithm was successful in recovering contours out of noisy slice samples of a surface, for the Hand, Pelvis and Skull data sets -- As a test for the correctness of the obtained curves in the slice levels, they were input into an algorithm of surface reconstruction, leading to a reconstructed surface which reproduces the topological and geometrical properties of the original object -- The algorithm robustly reacts not only to statistical noncorrelation at the self-intersections(nonmanifold neighborhoods) but also to occasional high noise at the nonselfintersecting (1manifold) neighborhoodsapplication/pdfengSpringer Berlin HeidelbergThe Visual Computer, Collection: Computer Science, Volume 27, Issue 3, pp. 211-226http://dx.doi.org/10.1007/s00371-010-0527-xSpringer-Verlag 2010Acceso abiertohttp://purl.org/coar/access_right/c_abf2Ellipse-based Principal Component Analysis for Self-intersecting Curve Reconstruction from Noisy Point Setsinfo:eu-repo/semantics/articlearticleinfo:eu-repo/semantics/publishedVersionpublishedVersionArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1CURVAS PLANASCOLECTORES (INGENIERÍA)TOPOLOGÍAVARIEDADES (MATEMÁTICAS)CORRELACIÓN (ESTADÍSTICA)ANÁLISIS ESTOCÁSTICOFUNCIONES ELÍPTICASCurves, planeTopologyManifolds (Mathematics)Correlation (statistics)Stochastic analysisFunctions, ellipticCurvesplaneTopologyManifolds (Mathematics)Correlation (statistics)Stochastic analysisFunctionsellipticReconstrucción superficialNube de puntosUniversidad EAFIT. Departamento de Ingeniería MecánicaRuíz, O.Vanegas, C.Cadavid, C.Laboratorio CAD/CAM/CAEThe Visual Computer, Collection: Computer ScienceThe Visual Computer273211226ORIGINALellipse-based_principal_component_analysis_abstract_springer.pdfellipse-based_principal_component_analysis_abstract_springer.pdfAbstractapplication/pdf434611https://repository.eafit.edu.co/bitstreams/ed051b32-3399-4237-ac4b-d9b9f0c90c7b/download27c57ee2e2abdddeac12e4cf673df8deMD52ellipse-based_principal_component_analysis_incomplete.pdfellipse-based_principal_component_analysis_incomplete.pdfVersión incompletaapplication/pdf2769352https://repository.eafit.edu.co/bitstreams/acae92ca-193d-416a-8b1a-6eeef69707ea/downloada2aade6429170950bac1f705e1bf6574MD53s00371-010-0527-x.pdfs00371-010-0527-x.pdfapplication/pdf3662681https://repository.eafit.edu.co/bitstreams/9b3bfd7b-1b8c-44e1-bb04-bce5dd42d2a6/downloadded7104fc34db6ecdf18ea5d6094bcf9MD54LICENSElicense.txtlicense.txttext/plain; charset=utf-82556https://repository.eafit.edu.co/bitstreams/74cbcf4b-8e6c-43ea-8a09-d0904fa64050/download76025f86b095439b7ac65b367055d40cMD5110784/9681oai:repository.eafit.edu.co:10784/96812022-11-08 11:36:09.82open.accesshttps://repository.eafit.edu.coRepositorio Institucional Universidad EAFITrepositorio@eafit.edu.co