Local Analysis as Grouping
in Measurement Space.
Another approach towards segmentation is based on a local analysis of
the raw data, explicitly utilizing the spatial information content of the
data set. Within this approach, objects are defined as areas being enclosed
by borders, i.e., strong local signal variations. However, a local analysis
can yield only local data, i.e. edge elements. Thus in a second processing
step these edge elements have to be grouped into continuous and closed
borderlines defining valid object regions. The edge-grouping process has
to delete edge elements not consistent with other data, and to create some
missing edge elements in order to close boundaries (figure 1.c).
This requires, of course, some global knowledge about the borderlines present.
Figure 1: The image a) used for testing segmentation algorithms.
It consist of several simple objects, with gaussian distributed noise added.
In b) the results of a simple cluster algorithm are displayed, an edge-detection
scheme was employed in c).
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