The ability to grow extra nodes is a potentially useful facility for a self-organising neural network. A network that can add nodes into its map space can approximate the input sp...
Stephen Marsland, Jonathan Shapiro, Ulrich Nehmzow
—It is well-known that the linear scale-space theory in computer vision is mainly based on the Gaussian kernel. The purpose of the paper is to propose a scale-space theory based ...
: Many classification problems involve high dimensional inputs and a large number of classes. Multiclassifier fusion approaches to such difficult problems typically centre around s...
We analyze theoretically the subspace best approximating images of a convex Lambertian object taken from the same viewpoint, but under different distant illumination conditions. Si...
Previous analyses of scalable streaming protocols for delivery of stored multimedia have largely focused on how the server bandwidth required for full-file delivery scales as the ...