Non-rigid registration requires a smoothness or regularization term for making the warp field regular. Standard models in use here include b-splines and thin plate splines. In this paper, we suggest a regularizer which is based on first principles, is symmetric with respect to source and destination, and fulfills a natural semi-group property for warps. We construct the regularizer from a distr...

This paper describes a method for estimating insurance claims or risk premiums allowing for spatial dependence as well as explanatory factors. The concepts are related to those used for graduation of mortality tables using cubic splines but extended to spatial dependence as found in many insurance products. Firstly techniques for measuring spatial dependence are reviewed. Then models with bivar...

In recent years, research efforts to extend linear metric learning models to handle nonlinear structures have attracted great interests. In this paper, we propose a novel nonlinear solution through the utilization of deformable geometric models to learn spatially varying metrics, and apply the strategy to boost the performance of both kNN and SVM classifiers. Thin-plate splines (TPS) are chosen...

The paper discusses the sensitivity of the height assignment of optically thin cirrus clouds to satellite calibration errors. Thin cirrus are often used as tracers for the derivation of high-level wind vectors. The height assignment is based on bi-spectral techniques, which in the case of Meteosat use both the IR (10.5 12.5 μm) and WV (5.7 7.1 μm) channels. Height allocation errors for typical ...

Image registration is inherently ill-posed, and lacks a unique solution. In the context of medical applications, it is desirable to avoid solutions that describe physically unsound deformations within the patient anatomy. Among the accepted methods of regularizing non-rigid image registration to provide solutions applicable to medical practice is the penalty of thin-plate bending energy. In thi...

In this paper, we develop a fast algorithm for a smoothing spline estimator in multivariate regression. To accomplish this, we employ general concepts associated with roughness penalty methods in conjunction with the theory of radial basis functions and reproducing kernel Hilbert spaces. It is shown that through the use of compactly supported radial basis functions it becomes possible to recove...

Two methods for registering laser-scans of human heads and transforming them to a new semantically consistent topology defined by a user-provided template mesh are described. Both algorithms are stated within the Iterative Closest Point framework. The first method is based on finding landmark correspondences by iteratively registering the vicinity of a landmark with a re-weighted error function...

We present in this paper an algorithm for surface reconstruction using thin plate splines on scattered patches or points on smooth surfaces. The algorithm is an improved version of Szeliski’s Variational Spline Fitting algorithm (SVSF). In particular, we introduce a different derivation of the discrete equations for the energy corresponding to the thin plat model. The results obtained on simula...

Approximation/interpolation from spaces of positive definite or conditionally positive definite kernels is an increasingly popular tool for the analysis and synthesis of scattered data and is central to many meshless methods. For a set of N scattered sites, the standard basis for such a space utilizes N globally supported kernels; computing with it is prohibitively expensive for large N . Easil...

MRI is unique in its ability to non-invasively and selectively alter tissue magnetization, and create grid patterns on a soft, deforming body such as the heart muscle. We track motion of myocardial tag lines with energy. minimizing B-spline snakes. Oriented tag templates are first constructed by simulating the MR imaging process. The templates are subsequently correlated with the image data, gi...