Hierarchical Data Representations Based on Planar Voronoi Diagrams

In the first pro- cess, the mosaic image is automatically generated by creat- ing the optimal Voronoi diagram so that the error between the original image and the resulting

We have presented a framework for computing distance transforms, Voronoi diagrams, and skeletons of generalized metrics using graphics hardware. When compared to the most

However, because one of the big advantages of using Voronoi diagrams is the spatial tessellations for local regions around the site points, the algorithm is particularly

Dey and his co-authors [DGS05, DS05] have suggested a Voronoi/Delaunay based method for estimating normals from noisy point cloud data.. The centrality of the normal estimation step

From left to right: input data points (black), all poles are ex- tracted and classified from the Voronoi diagram (red inside, green outside), poles are filtered, poles are

To place skeletons of elements, our method first places cristae, sulci and pockets based on anisotropic Voronoi diagram, then adds new lines for the wrinkles and new points for

Assigning an error metric to the current transform 6.. Minimizing the error

Given a set of input camera parame- ters, feature tracks and scene structure, the user is able to generate a scalar field visualization, based on an angular er- ror metric, along