In this talk I will define stability and show the existence and uniqueness theorem. A simple and fast algorithm will be given for finding stable matching under the ordering constraint. The algorithm has no parameters except for matching window size and disparity search interval. I will show on real examples how well stable matching can cope with very wide disparity search interval and half-occluded regions. I will also show stable matching is not biased by large objects in the scene which means it captures fine details and does not miss small objects.
I will then generalize the result to epsilon-delta stable matching which (is supposed to) possess robust properties. Finally, I will go even further and outline the avenues stability principle opens up for sparse matching problems, multicriterial or multisource matching, non-unique matchings, and in matchings based on a combination of local rules rather than on classical correlation measures.