Relaxation Labeling Techniques

Relaxation labeling techniques can be applied to many areas of computer vision.

Relaxation techniques have been applied to many other areas of computation in general, particularly to the solution of concurrent nonlinear equations.

The basic principles behind relaxation labeling methods are discuss various applications.

The basic elements of the relaxation labeling method are a set of features belonging to an object and a set of labels.

In the framework of vision, these features are usually points, edges and surfaces.

Normally, the labeling scheme used are probabilistic for each feature, weights or probabilities are assigned to each label in the set giving to the particular label is the correct one for that feature.

Probabilistic approaches are subsequently used to maximize (or minimize) the probabilities by iterative adjustment, taking into account the probabilities associated with neighboring features.

Relaxation strategies do not necessarily guarantee convergence, then we should not arrive at a final labeling solution with a unique label having probability one for each another.

The labeling process starts with an initial, and perhaps randomly, obligation of probabilities for each label

The basic algorithm then transforms these probabilities into to a new set according to some relaxation schedule.

This process is repeated until the labeling method converges or stabilizes.

Applications:

Relaxation technique, predating computer vision work, it has been used as the basis of iterative solutions of systems of equations, solving layout problems, breaking codes and many other applications. In computer vision it has been applied to:

Region based Segmentation

Edge point detection

Curved boundary extraction

Matching stereo pairs

Handwriting interpretation etc.,