Abstract
In this paper, a clustering method based on an improved 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 domains 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 in order to obtain the desirable clusters. First, clusters are created on demand and updated in recursive form. However, since a number of redundant clusters may be created, the dispensable clusters are secondly vanished by the competition among the created clusters; accordingly the remaining clusters are gradually agglomerated. Consequently, since the only appropriate clusters are remained, the segmentation can be achieved by assigning the data points to these clusters.
