Line Labeling Using Markov Random Fields

The task of obtaining a line labeling from a greyscale image of trihedral objects presents difficulties not found in the classical line labeling problem. As originally formulated, the line labeling problem assumed that each junction was correctly pre-classified as being of a particular junction type (e.g. T, Y, arrow); the success of the algorithms proposed have depended critically upon getting this initial junction classification correct. In real images, however, junctions of different types may actually look quite similar, and this pre-classification is often difficult to achieve. This issue is addressed by recasting the line labeling problem in terms of a coupled probabilistic system which labels both lines and junctions. This results in a robust system, in which prior knowledge of acceptable configurations can serve to overcome the problem of misleading or ambiguous evidence.

ICSI Technical Report TR-91-059

Technical Report