Watershed (image processing)

The watershed transformation treats the image it operates upon like a topographic map, with the brightness of each point representing its height, and finds the lines that run along the tops of ridges.

[3] Intuitively, a drop of water falling on a topographic relief flows towards the "nearest" minimum.

S. Beucher and F. Meyer introduced an algorithmic inter-pixel implementation of the watershed method,[5] given the following procedure: Previous notions focus on catchment basins, but not to the produced separating line.

One of the most common watershed algorithms was introduced by F. Meyer in the early 1990s, though a number of improvements, collectively called Priority-Flood, have since been made to this algorithm,[9] including variants suitable for datasets consisting of trillions of pixels.

During the successive flooding of the grey value relief, watersheds with adjacent catchment basins are constructed.

This flooding process is performed on the gradient image, i.e. the basins should emerge along the edges.

Either the image must be pre-processed or the regions must be merged on the basis of a similarity criterion afterwards.

Watersheds as optimal spanning forest have been introduced by Jean Cousty et al.[12] They establish the consistency of these watersheds: they can be equivalently defined by their “catchment basins” (through a steepest descent property) or by the “dividing lines” separating these catchment basins (through the drop of water principle).

In 2007, C. Allène et al.[13] established links relating Graph Cuts to optimal spanning forests.