Method for segmentation in an n-dimensional feature space and method for classifying objects in an n-dimensional data space which are segmented on the basis of geometric characteristics

Abstract

A segmentation method includes several steps wherein a single data space is selected by the user in an n-dimensional feature space in a first step. This selected data space is basically interpreted by the system as containing at least two classes of objects to be segmented. In the following steps, the system first determines a separation function in the n-dimensional feature space for differentiating the at least two classes and then applies this separation function to the entire data space or a large part of the data space. The segmentation result is then visually presented to the user in real time. The invention also relates to a method for classifying objects on the basis of geometric characteristics of objects previously segmented according to any method in an n-dimensional data space. In a first step, at least two objects are selected as representatives of two different categories, then a number (m) of geometric characteristics per object is determined by calculating various whole-number wave functions. Then, the objects are classified on the basis of the defined number of geometric characteristics or partial quantities. The previously required segmentation of the objects can be carried out according to the inventive method.