161 research outputs found
Bivariate hierarchical Hermite spline quasi--interpolation
Spline quasi-interpolation (QI) is a general and powerful approach for the
construction of low cost and accurate approximations of a given function. In
order to provide an efficient adaptive approximation scheme in the bivariate
setting, we consider quasi-interpolation in hierarchical spline spaces. In
particular, we study and experiment the features of the hierarchical extension
of the tensor-product formulation of the Hermite BS quasi-interpolation scheme.
The convergence properties of this hierarchical operator, suitably defined in
terms of truncated hierarchical B-spline bases, are analyzed. A selection of
numerical examples is presented to compare the performances of the hierarchical
and tensor-product versions of the scheme
Bases of T-meshes and the refinement of hierarchical B-splines
In this paper we consider spaces of bivariate splines of bi-degree (m, n)
with maximal order of smoothness over domains associated to a two-dimensional
grid. We define admissible classes of domains for which suitable combinatorial
technique allows us to obtain the dimension of such spline spaces and the
number of tensor-product B-splines acting effectively on these domains.
Following the strategy introduced recently by Giannelli and Juettler, these
results enable us to prove that under certain assumptions about the
configuration of a hierarchical T-mesh the hierarchical B-splines form a basis
of bivariate splines of bi-degree (m, n) with maximal order of smoothness over
this hierarchical T-mesh. In addition, we derive a sufficient condition about
the configuration of a hierarchical T-mesh that ensures a weighted partition of
unity property for hierarchical B-splines with only positive weights
Discontinuity Detection by Null Rules for Adaptive Surface Reconstruction
We present a discontinuity detection method based on the so-called null rules, computed as a vector in the null space of certain collocation matrices. These rules are used as weights in a linear combination of function evaluations to indicate the local behavior of the function itself. By analyzing the asymptotic properties of the rules, we introduce two indicators (one for discontinuities of the function and one for discontinuities of its gradient) by locally computing just one rule. This leads to an efficient and reliable scheme, which allows us to effectively detect and classify points close to discontinuities. We then show how this information can be suitably combined with adaptive approximation methods based on hierarchical spline spaces in the reconstruction process of surfaces with discontinuities. The considered adaptive methods exploit the ability of the hierarchical spaces to be locally refined, and fault detection is a natural way to guide the refinement with low computational cost. A selection of test cases is presented to show the effectiveness of our approach
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