Object subsampling strategies to improve computational performance

We investigate object simplification methods based on Centroidal Voronoi Tesselation (CVT) that share the possibility of great speed-ups in various scenarios. We propose Constrained CVT to sample points from the object boundary and the Region-based CVT to be able to subsample lower dimensional objects, as well. Moreover we introduce custom weight functions based on object properties. Thus, we can be more specific on what are the important parts of the subsampled object. We also list several novel applications corresponding to the theoretical achievements presented. The advantages of applying the subsampling strategies are presented for registration, human detection, and the segmentation of the retinal vascular system, respectively. Quantitative results are shown to check the deterioration of the accuracy with the level of subsampling, and the computational gain. We also make comparisons with other naive (e.g. random) subsampling methods.