Before customizing your search settings, it is necessary to
understand how a match between your model and occurrences in the
target is determined. The Score and Target score are the primary
factors in determining which occurrences are considered matches
with the models in your ModelFinder step.
The Score is
a measure of the active edges in the model found in the occurrence,
weighted by the deviation in position of these common edges. Active
edges in the model not found in the occurrence reduce the
score.
The Target
score is a measure of edges found in the occurrence that are
not present in the original model (that is, extra edges), weighted
by the deviation in position of the common edges. Edges found in
the occurrence that are not present in the model will reduce the
target score.
The model scores are calculated as follows:
The model coverage, target coverage, and fit error components of
the score and target score are explained below.
The Fit error
is a measure of how well the edges of the occurrence correspond to
those of the model. The fit error is calculated as the average
quadratic distance, in pixels or calibrated units, between the
edgels in the occurrence and the corresponding active edgels in the
model:
A perfect fit gives a fit error of 0.0. The fit error weighting
factor (between 0.0 - 100.0) determines the importance to place on
the fit error when calculating the score and target score.
The following diagram illustrates how the model coverage, target
coverage, and fit error work together to provide details about the
nature of a result occurrence.