We propose an algorithm that groups points similarly to how human observers do. It is simple, totally unsupervised and able to find clusters of complex and not necessarily convex s...
This paper proposes new methods to answer approximate nearest neighbor queries on a set of n points in d-dimensional Euclidean space. For any xed constant d, a data structure with...
For a given point set in Euclidean space we consider the problem of finding (approximate) nearest neighbors of a query point but restricting only to points that lie within a fixed...
Stefan Funke, Theocharis Malamatos, Domagoj Matije...
Abstract Multiresolution models support the interactive visualization of large volumetric data through selective refinement, an operation which permits to focus resolution only on...
Emanuele Danovaro, Leila De Floriani, Paola Magill...
Delaunay tessellations and Voronoi diagrams capture proximity relationships among sets of points in any dimension. When point coordinates are not known exactly, as in the case of ...