Abstract
Objects usually consist of parts and the question arises whether there are perceptual features which allow breaking down an object into its fundamental parts without any additional (e.g., functional) information. As in the first paper of this sequence, we focus on the division of our world along convex to concave surface transitions. Here we are using machine vision to produce convex segments from 3D-scenes. We assume that a fundamental part is one, which we can easily name while at the same time there is no natural subdivision possible into smaller parts. Hence in this experiment we presented the computer vision generated segments to our participants and asked whether they can identify and name them. Additionally we control against segmentation reliability and we find a clear trend that reliable convex segments have a high degree of name-ability. In addition, we observed that using other image-segmentation methods will not yield nameable entities. This indicates that convex-concave surface transition may indeed form the basis for dividing objects into meaningful entities. It appears that other or further subdivisions do not carry such a strong semantical link to our everyday language as there are no names for them.