Optimal designs for 3D shape analysis with spherical harmonic descriptors

dc.contributor.authorDette, Holgerde
dc.contributor.authorMelas, Viatcheslav B.de
dc.contributor.authorPepelyshev, Andreyde
dc.date.accessioned2004-12-06T18:39:14Z
dc.date.available2004-12-06T18:39:14Z
dc.date.issued2004de
dc.description.abstractWe determine optimal designs for some regression models which are frequently used for describing 3D shapes. These models are based on a Fourier expansion of a function defined on the unit sphere in terms of spherical harmonic basis functions. In particular it is demonstrated that the uniform distribution on the sphere is optimal with respect to all Φp-criteria proposed by Kiefer (1974) and also optimal with respect to a criterion which maximizes a p-mean of the r smallest eigenvalues of the variance-covariance matrix. This criterion is related to principal component analysis, which is the common tool for analyzing this type of image data. Moreover, discrete designs on the sphere are derived, which yield the same information matrix in the spherical harmonic regression model as the uniform distribution and are therefore directly implementable in practice. It is demonstrated that the new designs are substantially more efficient than the commonly used designs in 3D-shape analysis.en
dc.format.extent1635267 bytes
dc.format.extent377507 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/postscript
dc.identifier.urihttp://hdl.handle.net/2003/4896
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-6667
dc.language.isoende
dc.publisherUniversitätsbibliothek Dortmundde
dc.subjectshape analysisen
dc.subjectspherical harmonic descriptorsen
dc.subjectoptimal designsen
dc.subjectquadrature formulasen
dc.subjectprincipal component analysisen
dc.subject3D-image dataen
dc.subject.ddc310de
dc.titleOptimal designs for 3D shape analysis with spherical harmonic descriptorsen
dc.typeTextde
dc.type.publicationtypereporten
dcterms.accessRightsopen access

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