Relaxation methods have been particularly applied to optimisation problems. Such problems are very common in computer vision and so relaxation methods have been applied in a wide variety of ways to computer vision.

Relaxation methods have been applied to:

**Edge linking**- -- The
probabilities of edge points lying on particular edges is
determined by considering neighbouring edge points. Different
labels are used for each edge, and a relaxation schedule is then
used to find the appropriate label for each edge point.

**Line labelling techniques**-
can be expressed a relaxation problem.
- Label lines as belonging to a certain class of edge (occluding, concave or convex).
- Probabilities can be assigned to each type of labelling fairly easily.
- Only certain sets of edge labellings are mutually compatible at line junctions.
- These restrictions can be expressed as constraints when the conditional probabilities for the particular labels are estimated.

**Segmentation**- can be interpreted in two
slightly different ways here:
- The process of grouping pixels into regions of similarity. Here the relaxation processes amount to self-organisation of the image. The regions are simply labelled as .
- The process of labelling regions of image as belonging to recognised physical entities such as sky, grass, trees, car and road.

dave@cs.cf.ac.uk