Mesh smoothing is a method to remove noise or small scale features from large meshes, while still preserving the basic overall shape and important features of the original model. Most sophisticated smoothing methods rely on ideas from differential geometry. In this lecture we discuss some basic geometric concepts and introduce some simple smoothing algorithms; the paper presented at this lectur...

Ultrasound imaging is an important technique with great potential for accurate nondestructive testing. Previously, we proposed a high-resolution 3-D imaging algorithm for UWB radar, the Envelope algorithm with adaptive smoothing techniques. The Envelope imaging algorithm utilizes the principle that the target boundary is expressed as an envelope of spheres determined by observed ranges, while t...

This paper examines the application of a bootstrap test error estimation of radial basis functions, specifically thin-plate spline fitting, in surface smoothing. The presence of noisy data is a common issue of the point set model that is generated from 3D scanning devices, and hence, point set denoising is one of the main concerns in point set modelling. Bootstrap test error estimation, which i...

This paper introduces five new univariate exponentially weighted methods for forecasting intraday time series that consist of both intraweek and intraday seasonal cycles. Applications of relevance include forecasting volumes of call centre arrivals, transportation, e-mail traffic and electricity load. The first method that we develop extends an exponential smoothing formulation that has been us...

This paper describes a new surface normal smoothing process which can be used in conjunction with shape-fromshading. Rather than directly smoothing the surface normal vectors, we exert control over their directions by smoothing the field of principal curvature vectors. To do this we develop a topography sensitive smoothing process which overcomes the problems of singularities in the field of pr...

Structure-preserving image smoothing aims to extract semantically meaningful image structure from texture, which is one of the fundamental problems in computer vision and graphics. However, it is still not clear how to define this concept. On the other hand, semantic image labeling has achieved significant progress recently and has been widely used in many computer vision tasks. In this paper, ...