This paper presents a framework for multiresolution compression and geometric reconstruction of arbitrarily dimensioned data designed for distributed applications. Although being ...
We present a manifold learning approach to dimensionality
reduction that explicitly models the manifold as a mapping
from low to high dimensional space. The manifold is
represen...
The work described here extends the power of 2D animation with a form of texture mapping conveniently controlled by line drawings. By tracing points, line segments, spline curves,...
Abstract. Volume segmentation is a relatively slow process and, in certain circumstances, the enormous amount of prior knowledge available is underused. Model-based liver segmentat...
Charles Florin, Nikos Paragios, Gareth Funka-Lea, ...
We propose an energy-based framework for approximating surfaces from a cloud of point measurements corrupted by noise and outliers. Our energy assigns a tangent plane to each (noi...