# Generalized Pattern Origin

As described in the preceding topic, PatMax returns a transformation object that describes how the pattern in the run-time image is different from the trained pattern. The returned transformation object describes the transformation between the trained pattern and the pattern in the run-time image, as shown in the following figure.

In some cases, you may want to apply a transformation to the trained pattern before you search for the pattern in a run-time image. You apply such a transformation by supplying a generalized pattern origin. PatMax applies the transformation you supply to the trained pattern before it searches for the pattern in the run-time image. When it returns a pattern location result, it returns the transformation between the trained, transformed, pattern and the pattern in the run-time image.

The following figure shows how you use a generalized pattern origin to scale a trained pattern before locating it in the run-time image.

You typically supply a generalized pattern origin to compensate for known scale or rotation changes in the trained pattern. If you supply a generalized pattern origin you should keep the following points in mind:

- PatMax will return results that describe the difference between the trained pattern after it has been transformed by the generalized pattern origin and the pattern in the run-time image.
- Any zone ranges and nominal values that you specify are interpreted with respect to the transformed trained pattern.
- Supplying a generalized pattern origin has no effect on the speed, accuracy, or number of results produced by PatMax.
- The x- and y-translation components of a generalized pattern origin are equivalent to the simple (point) origin.