Simplification of objects for adaptive matching and visualization

In this paper, we present a method for the simplification of the geometry of 2D and 3D objects. Our apporach allows us to perform both uniform and non-uniform simplification. Our purposes are twofold. On the one hand, we can make matching algorithms faster in object detection using simplified geometries. Moreover, with non-uniform simplification we can adjust the quality of the visualization of the object details regarding the importance of the different parts of the object. In this way, we improve computational performance since less template points are matched using a simplified template, and some information can be handled as less important in visualization. We demonstrate how we can take advantage of selecting special weight functions for matching and visualization purposes besides the uniform one. The theoretical background of the simplification is derived through the centroidal Voronoi tessellation framework.