# Tuning Elasticity

A feature in one training image is considered to be the same as a feature in another training image if it appears in the "same" location after the two images have been aligned. You can use the distance tolerance (TuningElasticity) to control how far away a matching feature can be, in pixels, and still be considered to be in the "same" location.

For example, with the feature shown below, the capture range would look as follows:

This parameter can be used mainly for speed, reducing the number of correspondences to consider. The internal feature matcher will always prefer closer correspondences. It is far more convenient to specify this value in pels, so that a standard default can be meaningful for many applications.

Composite pattern training may generate synthetic pattern features that do not exactly correspond to the features in any specific training image.

Note: This tool is not intended for use when the images contain fundamentally different models. In this case, the behavior is still as outlined, but this may produce a subtly unusable model. For example, you could create a single model that is the composite of three unique models, by setting the ImageFraction to approximately 0.25. However, to actually find instances of these models at run-time, the PatMax accept threshold would also have to be around 0.25, which likely would make it impossible to avoid spurious matches.