Spline approximation of a random process with singularity

Let a continuous random process $X$ defined on $[0,1]$ be $(m+\beta)$-smooth, $0\le m, 00$ and have an isolated singularity point at $t=0$. In addition, let $X$ be locally like a $m$-fold integrated $\beta$-fractional Brownian motion for all non-singular points. We consider approximation of $X$ by piecewise Hermite interpolation splines with $n$ free knots (i.e., a sampling design, a mesh). The approximation performance is measured by mean errors (e.g., integrated or maximal quadratic mean errors). We construct a sequence of sampling designs with asymptotic approximation rate $n^{-(m+\beta)}$ for the whole interval.Comment: 16 pages, 2 figure typos and references corrected, revised classes definition, results unchange

Anti-aliasing with stratified B-spline filters of arbitrary degree

A simple and elegant method is presented to perform anti-aliasing in raytraced images. The method uses stratified sampling to reduce the occurrence of artefacts in an image and features a B-spline filter to compute the final luminous intensity at each pixel. The method is scalable through the specification of the filter degree. A B-spline filter of degree one amounts to a simple anti-aliasing scheme with box filtering. Increasing the degree of the B-spline generates progressively smoother filters. Computation of the filter values is done in a recursive way, as part of a sequence of Newton-Raphson iterations, to obtain the optimal sample positions in screen space. The proposed method can perform both anti-aliasing in space and in time, the latter being more commonly known as motion blur. We show an application of the method to the ray casting of implicit procedural surfaces

Error analysis for quadratic spline quasi-interpolants on non-uniform criss-cross triangulations of bounded rectangular domains

Given a non-uniform criss-cross partition of a rectangular domain $\Omega$, we analyse the error between a function $f$ defined on $\Omega$ and two types of $C^1$-quadratic spline quasi-interpolants (QIs) obtained as linear combinations of B-splines with discrete functionals as coefficients. The main novelties are the facts that supports of B-splines are contained in $\Omega$ and that data sites also lie inside or on the boundary of $\Omega$. Moreover, the infinity norms of these QIs are small and do not depend on the triangulation: as the two QIs are exact on quadratic polynomials, they give the optimal approximation order for smooth functions. Our analysis is done for $f$ and its partial derivatives of the first and second orders and a particular effort has been made in order to give the best possible error bounds in terms of the smoothness of $f$ and of the mesh ratios of the triangulation

Fast adaptive elliptical filtering using box splines

We demonstrate that it is possible to filter an image with an elliptic window of varying size, elongation and orientation with a fixed computational cost per pixel. Our method involves the application of a suitable global pre-integrator followed by a pointwise-adaptive localization mesh. We present the basic theory for the 1D case using a B-spline formalism and then appropriately extend it to 2D using radially-uniform box splines. The size and ellipticity of these radially-uniform box splines is adaptively controlled. Moreover, they converge to Gaussians as the order increases. Finally, we present a fast and practical directional filtering algorithm that has the capability of adapting to the local image features.Comment: 9 pages, 1 figur