Empirical and theoretical proof for why Determine of Advantage (FOM) is the very best edge-detection analysis metric

Picture segmentation and edge detection are intently associated duties. Take this output from a coastal segmentation mannequin for instance:

The mannequin will classify each pixel as both land or ocean (segmentation masks). Then the shoreline is the pixels the place this classification adjustments (edge map). Basically, edge detection will be performed utilizing the boundaries of the output of a picture segmentation mannequin.

I needed to make use of this relationship in my analysis to assist consider coastal picture segmentation fashions. Related analysis all use confusion matrix-based metrics like accuracy, precision and recall. These evaluate all pixels in a predicted segmentation masks to a floor reality masks.

The issue is these may overestimate efficiency in crucial area — the shoreline.

The vast majority of pixels are in the midst of the ocean or utterly surrounded by land. This makes them simpler to categorise than these near the shoreline. You’ll be able to see this in Determine 2. Sadly, these errors could also be shrouded within the sea of accurately categorised pixels.