Empirical and theoretical proof for why Determine of Benefit (FOM) is the most effective edge-detection analysis metric
Picture segmentation and edge detection are carefully 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). Generally, edge detection will be finished utilizing the boundaries of the output of a picture segmentation mannequin.
I wished to make use of this relationship in my analysis to assist consider coastal picture segmentation fashions. Comparable analysis all use confusion matrix-based metrics like accuracy, precision and recall. These evaluate all pixels in a predicted segmentation masks to a floor fact masks.
The issue is these may overestimate efficiency in a very powerful area — the shoreline.
The vast majority of pixels are in the midst of the ocean or fully surrounded by land. This makes them simpler to categorise than these near the shoreline. You may see this in Determine 2. Sadly, these errors could also be shrouded within the sea of appropriately categorized pixels.
