Abstract
In this paper, a clustering method based on an improved competitive process is proposed to segment the entire circumferential range data significantly. 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 regions 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 the following two stages. First, clusters are created on demand by using random sampling techniques. However, since a number of redundant clusters may be created, the dispensable clusters are secondly vanished by the competitive process that is realized by an optimal iteration time determined using MML criterion ;accordingly the remaining clusters are gradually agglomerated. Consequently, since the only appropriate clusters are remaining, the segmentation can be achieved by assigning the data points to these clusters.
