# Patterns Trained From Acquired Images

As the size of scale change between the trained pattern and the run-time pattern increases, PatMax's performance can begin to degrade. In general, scale changes below 10% to 20% have no effect on PatMax's ability to discriminate patterns within a run-time image, and no effect on PatMax's ability to locate patterns with ultrahigh precision (better than 0.10 pixels).

As scale change increases above 10% to 20%, some applications will experience a reduction in accuracy levels.

At extremely large scale changes (greater than a factor of 2), some applications will experience problems discriminating patterns in run-time image.

PatMax is more likely to be able to successfully handle large scale changes when the overall shape of the trained pattern does not change at different granularities. The following figure shows an example of a pattern whose shape changes at different granularities.

The pattern shown in this figure may be more difficult for PatMax to find at extreme scale change.

The pattern shown in the following figure has the same shape at different granularities. This pattern may tend to work better with extreme scale changes.

Note: If the run-time pattern is less than about one third the size of the trained pattern, the coverage score it receives will be reduced. This reduced coverage score, in turn, lowers the overall score received by the pattern, even though the shape of the pattern may be a good match with the trained pattern.