Scalable discrepancy measures for segmentation evaluation

We propose a set of scalable discrepancy measures that may be applied for segmentation evaluation when a reference is known. The proposed measures take into account under and over detected points within an adjustable area. They give the intensity of the discrepancy and its relative position. Furthermore a scale parameter allows the accuracy of the measures to be adjusted.

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