Determining what is a match

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:

• Score = Model Coverage x (1 - (Fit Error Weighting Factor x Normalized Fit Error)).

• Target Score = Target Coverage x (1 - (Fit Error Weighting Factor x Normalized Fit Error)).

The model coverage, target coverage, and fit error components of the score and target score are explained below.

The model coverage and target coverage are defined as follows:

• Model coverage. The model coverage is the percentage of the total length of the model's active edges found in the occurrence. 100% indicates that for each of the model's active edges, a corresponding edge was found in the occurrence.

• Target coverage. The target coverage is the percentage of the total length of edges present within the occurrence's bounding box, corresponding to the model's active edges. Thus, a target coverage score of 100% means that no extra edges were found. Lower scores indicate that features or edges found in the target (result occurrence) are not present in the model.

  

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.