Quantitative analysis of the reconstruction performance of interpolants

The analysis presented provides a quantitative measure of the reconstruction or interpolation performance of linear, shift-invariant interpolants. The performance criterion is the mean square error of the difference between the sampled and reconstructed functions. The analysis is applicable to reconstruction algorithms used in image processing and to many types of splines used in numerical analysis and computer graphics. When formulated in the frequency domain, the mean square error clearly separates the contribution of the interpolation method from the contribution of the sampled data. The equations provide a rational basis for selecting an optimal interpolant; that is, one which minimizes the mean square error. The analysis has been applied to a selection of frequently used data splines and reconstruction algorithms: parametric cubic and quintic Hermite splines, exponential and nu splines (including the special case of the cubic spline), parametric cubic convolution, Keys' fourth-order cubic, and a cubic with a discontinuous first derivative. The emphasis in this paper is on the image-dependent case in which no a priori knowledge of the frequency spectrum of the sampled function is assumed

Warped Functional Analysis of Variance

This article presents an Analysis of Variance model for functional data that explicitly incorporates phase variability through a time-warping component, allowing for a unified approach to estimation and inference in presence of amplitude and time variability. The focus is on single-random-factor models but the approach can be easily generalized to more complex ANOVA models. The behavior of the estimators is studied by simulation, and an application to the analysis of growth curves of flour beetles is presented. Although the model assumes a smooth latent process behind the observed trajectories, smoothness of the observed data is not required; the method can be applied to the sparsely observed data that is often encountered in longitudinal studies

08221 Abstracts Collection -- Geometric Modeling

From May 26 to May 30 2008 the Dagstuhl Seminar 08221 ``Geometric Modeling\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Interpolation by spline spaces on classes of triangulations

We describe a general method for constructing triangulations Δ which are suitable for interpolation by Srq(Δ),

The Akima's fitting method for quartic splines

For the Hermite type quartic spline interpolating on the partition knots and at the midpoint of each subinterval, we consider the estimation of the derivatives on the knots, and the values of these derivatives are obtained by constructing an algorithm of Akima's type. For computing the derivatives on endpoints are also considered alternatives that request optimal properties near the endpoints. The error estimate in the interpolation with this quartic spline is generally obtained in terms of the modulus of continuity. In the case of interpolating smooth functions, the corresponding error estimate reveal the maximal order of approximation O(h^3). A numerical experiment is presented for making the comparison between the Akima's cubic spline and the Akima's variant quartic spline having deficiency 2 and natural endpoint conditions

Comparison and Evaluation of Didactic Methods in Numerical Analysis for the Teaching of Cubic Spline Interpolation

In mathematical education it is crucial to have a good teaching plan and to execute it correctly. In particular, this is true in the field of numerical analysis. Every teacher has a different style of teaching. This thesis studies how the basic material of a particular topic in numerical analysis was developed in four different textbooks. We compare and evaluate this process in order to achieve a good teaching strategy. The topic we chose for this research is cubic spline interpolation. Although this topic is a basic one in numerical analysis it may be complicated for students to understand. The aim of the thesis is to analyze the effectiveness of different approaches of teaching cubic spline interpolation and then use this insight to write our own chapter. We intend to channel every-day thinking into a more technical/practical presentation of a topic in numerical analysis. The didactic methodology that we use here can be extended to cover other topics in numerical analysis.Methods of teaching mathematics are different for several reasons, for example, the presentation style of teacher of a particular topic. In several books we can observe a different approach of presentation material of a topic, and at the end we can produce a unique way of teaching but in a different way. In our thesis we study different approaches to teaching in a several numerical analysis books in the topic of cubic spline interpolation. What is cubic spline interpolation? Cubic spline interpolation is a type of interpolation of data points. Interpolation is a method of constructing a curve between some data points. We chose cubic spline interpolation because it is better than other kinds of interpolation. Cubic spline interpolation has a smaller curvature compared with other types of interpolation. Therefore, cubic spline interpolation produces a smooth curve. In this research we study different approaches of teaching cubic spline interpolation to find a good way for presenting the cubic spline interpolation topic, because this topic may be complicated for students to understand. To reach a good process of presentation of cubic spline interpolation we compare each part of different approaches in the books we have studied for teaching cubic spline interpolation by asking questions and then answering those questions. In this way we will show how we can evaluate each answer. Evaluating each answer we will obtain a good result which will prepare us for writing our own chapter in order to present cubic spline interpolation in our way

Bivariate Interpolation by Splines and Approximation Order

We construct Hermite interpolation sets for bivariate spline spaces of arbitrary degree and smoothness one on non-rectangular domains with uniform type triangulations. This is done by applying a general method for constructing Lagrange interpolation sets for bivariate spline spaecs of arbitrary degree and smoothness. It is shown that Hermite interpolation yields (nearly) optimal approximation order. Applications to data fitting problems and numerical examples are given

Wiener splines

We describe an alternative way of constructing interpolating B-spline curves, surfaces or volumes in Fourier space which can be used for visualization. In our approach the interpolation problem is considered from a signal processing point of view and is reduced to finding an inverse B-spline filter sequence. The Fourier approach encompasses some advantageous features, such as successive approximation, compression, fast convolution and hardware support. In addition, optimal Wiener filtering can be applied to remove noise and distortions from the initial data points and to compute a smooth, least-squares fitting ‘Wiener spline’. Unlike traditional fitting methods, the described algorithm is simple and easy to implement. The performance of the presented method is illustrated by some examples showing the restoration of surfaces corrupted by various types of distortions