New Solutions for Surface Reconstruction from Discrete Point Data by Means of Computational Intelligence

dc.contributor.authorAlbersmann, Frankde
dc.contributor.authorDrerup, Peterde
dc.contributor.authorMehnen, Jörnde
dc.contributor.authorWeinert, Klausde
dc.date.accessioned2004-12-07T08:19:40Z
dc.date.available2004-12-07T08:19:40Z
dc.date.created1998de
dc.date.issued1998-11-08de
dc.description.abstractSurface reconstruction by means of triangulation of digitized point data leads to computational complex optimization problems. Here, deterministic algorithms often result in insufficient solutions or very long computation times. In this article, alternative methods of computational intelligence are discussed. A comparative analysis of two evolutionary algorithms applied to four different smoothness criteria for the triangulation of sparse point data sets is presented. optimally triangulated surfaces are the basis for many practical applications. The results presented here cover the efficient implementation and the influence of different triangulations for an adequate touch probe radius compensation (TPRC).en
dc.format.extent1752392 bytes
dc.format.extent297116 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/postscript
dc.identifier.urihttp://hdl.handle.net/2003/5350
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-14969
dc.language.isoende
dc.publisherUniversität Dortmundde
dc.relation.ispartofseriesReihe Computational Intelligence ; 22de
dc.subjectevolutionary algorithmsen
dc.subjecttouch probe radius compensationen
dc.subjecttriangulationen
dc.subject.ddc004de
dc.titleNew Solutions for Surface Reconstruction from Discrete Point Data by Means of Computational Intelligenceen
dc.typeTextde
dc.type.publicationtypereport
dcterms.accessRightsopen access

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