Segmentation of range data based on a stochastic clustering method with competitive process

In this paper, a stochastic clustering method with a competitive process is proposed to segment significantly the entire circumferential range data. The segmentation technique is utilized as the preprocessing of 3-D shape modeling so that the modeling can be more easily achieved for the object that has arbitrary topology, in which the data points are divided into the several subsets that represent the 3-D shapes of different quadric surfaces. The clustering method is implemented by evaluating a distance computed between each data point and each quadric surface. Furthermore, it consists of creation and competitive processes in order to obtain the desirable clusters. Consequently, since the only appropriate clusters are remaining, the segmentation can be achieved by assigning the data points to these clusters.