Data approximation using a blending type spline construction

Generalized expo-rational B-splines (GERBS) is a blending type spline construction where local functions at each knot are blended together by Ck-smooth basis functions. One way of approximating discrete regular data using GERBS is by partitioning the data set into subsets and fit a local function to...

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Hauptverfasser: Dalmo, Rune, Bratlie, Jostein
Format: Tagungsbericht
Sprache:eng
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Zusammenfassung:Generalized expo-rational B-splines (GERBS) is a blending type spline construction where local functions at each knot are blended together by Ck-smooth basis functions. One way of approximating discrete regular data using GERBS is by partitioning the data set into subsets and fit a local function to each subset. Partitioning and fitting strategies can be devised such that important or interesting data points are interpolated in order to preserve certain features.We present a method for fitting discrete data using a tensor product GERBS construction. The method is based on detection of feature points using differential geometry. Derivatives, which are necessary for feature point detection and used to construct local surface patches, are approximated from the discrete data using finite differences.
ISSN:0094-243X
1551-7616
DOI:10.1063/1.4902470