The features used in segmentation so far have been geometric or intensity gradient based.
Instead of trying to extract such high level features, another approach is to work directly with regions of pixels in the image, and to describe them by various statistical measures.
Such measures are usually represented by a single value.
These can be calculated as a simple by-product of the segmentation procedures previously described.
Such statistical descriptions may be divided into two distinct classes. Examples of each class are given below:
Some of the above measures have already been defined or have obvious meanings.
The others are:
We can form seven new moments from the central moments that are invariant to changes of position, scale and orientation of the object represented by the region (invariants will be discussed below shortly), although these new moments are not invariant under perspective projection. For moments of order up to three, these are:
We can also define eccentricity using moments as
We can also find principal axes of
inertia that define a natural coordinate
system for a region. Let
be given by
Most of the above measures may be useful in helping to control image reasoning and recognition, even if they are not sufficient for reasoning by themselves. However, these measures are very cheap to calculate as byproducts of the segmentation strategies detailed, especially region growing.