The main purpose of the proposed technique is to allow a blendshape rig to create facial expressions that are independent from the underlying blendshape poses. [JTDP03] proposed an automatic segmentation technique to divide the face into regions that have similar amount of deformation. Each region finds its best blendshape weights that conforms to the motion capture data and then propagates the...

Radial basis function (RBF) approximation is an extremely powerful tool for representing smooth functions in non-trivial geometries, since the method is meshfree and can be spectrally accurate. A perceived practical obstacle is that the interpolation matrix becomes increasingly illconditioned as the RBF shape parameter becomes small, corresponding to flat RBFs. Two stable approaches that overco...

This paper aims in comparing countries with different energy strategies, and demonstrate the close connection between environment and economic growth in the ex-Eastern countries, during their transition to market economies. We have developed a radial-basis function neural network system, which is trained to classify countries based on their emissions of carbon, sulphur and nitrogen oxides, and ...

Much work has been done on reconstructing arbitrary surfaces using the radial basis function (RBF) method, but one can hardly find any work done on the use of RBFs to solve partial differential equations (PDEs) on arbitrary surfaces. In this paper, we investigate methods to solve PDEs on arbitrary stationary surfaces embedded in R3 using the RBF method. We present three RBF-based methods that e...

Detection of rain/no-rain condition on the ground is an important for application of radar rainfall algorithms. A radial basis function (RBF) neural network-based scheme for rain/no-rain determination on the ground using vertical profiles of radar data is described in this paper. Evaluation based on WSR-88D radar over central Florida indicates that rain/no-rain condition can be inferred fairly ...

We present error bounds for the interpolation with anisotropically transformed radial basis functions for both function and its partial derivatives. The bounds rely on a growth function and do not contain unknown constants. For polyharmonic basic functions in R we show that the anisotropic estimates predict a significant improvement of the approximation error if both the target function and the...

Abstract. We explore a connection between Gaussian radial basis functions and polynomials. Using standard tools of potential theory, we find that these radial functions are susceptible to the Runge phenomenon, not only in the limit of increasingly flat functions, but also in the finite shape parameter case. We show that there exist interpolation node distributions that prevent such phenomena an...

On page 8 of the paper (page 3209 of the volume), in the seventh line of the second-to-last paragraph of section 5.

Résumé. Nous présentons un algorithme de SVM et des méthodes graphiques pour le traitement de grands ensembles de données. Pour pouvoir traiter de tels ensembles de données, nous utilisons une représentation des données de plus haut niveau (sous forme symbolique). L’algorithme de séparateur à vaste marge (SVM) est adapté pour pouvoir traiter ce nouveau type de données. Nous construisons un nouv...

In this paper, we prove convergence results for multiscale approximation using compactly supported radial basis functions restricted to the unit sphere, for target functions outside the reproducing kernel Hilbert space of the employed kernel.