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only to its object Each iteration, however, propagates the effect through its neighbor or related nodes, to other nodes that are not directly related The circle of influence of a node increases with each iteration This propagation of influence results in global consistency through direct local effects In most applications, some knowledge about the objects is available at the start of the labeling process Segmentation, or some other process which takes place before labeling, often gives information that can be used to refine the initial set Pi for a node This knowledge can be used to refine the initial label sets for objects The labeling process is then used to further refine these sets to yield a unique label for each object The labeling problem now may be considered in a slightly different form than the above Based on some unary relations, a label set Pl can be assigned to an object The correct label is uncertain However, a confidence value can be assigned to each label lk E Pl The confidence value, like a subjective probability, indicates a belief that the entity may be assigned this label based on the available evidence in the image Thus, for each element lk E Pi, a nonnegative probability Pik represents the confidence that the labellk is the correct label for node i This confidence value may be considered the membership value if approaches from fuzzy set theory are used in constraint propogation The task of the labeling process is to use the constraints to refine the



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(Figure 8-12)

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confidence value for each label The confidence value Pik is influenced by the confidence values in the labels of the connected nodes Thus, in the tth iteration, the confidence value P~kfor the label lk at node i is the function of the confidence value P~k1 and the confidence values of the labels of all directly related nodes In each iteration a node looks at the labels of all its related nodes and then uses the known constraints to update the confidence in its labels The process may terminate either when each node has been assigned a unique label or when the confidence values achieve a steady state Note that in place of just the presence or absence of a label, there is now a continuum of confidence values in a label for an object The above process, commonly called the relaxation labeling process, attempts to decide which of the possible interpretations is correct on the basis of local evidence Interestingly, though, the final interpretation is globally correct In each iteration, the confidence in a label is directly influenced only by directly related nodes However, this influence is propagated to other nodes in later iterations The sphere of influence increases with the number of iterations In relaxation labeling, the constraints are specified in terms of compatibility functions Suppose that objects Oi and OJ are related by ~j, and under this relation labels Lik and Ljl are highly "likely" to occur The knowledge about the likelihood of these labels can be expressed in terms of a function that will increase the confidence in these labels for the objects under consideration In such a situation, the presence of Lik at Oi encourages the assignment of Ljl to OJ It is also possible that the incompatibility of certain labels can be used to discourage labels by decreasing their confidence values In the following, we discuss an algorithm that uses relaxation labeling to determine disparity values in images The algorithm to determine optical flow, discussed later in this chapter, also is an example ofrelaxation labeling Disparity Computations as Relaxation Labeling.

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Listing 8-4

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The matching problem is to pair a point Pi = (Xi, Yi) in the first image with a point Pj = (Xj, Yj) in the second image The disparity between these points is the displacement vector between the two points: (1414) The result of matching is a set of conjugate pairs

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In any kind of matching problem, there are two questions that must be answered:

Figure 8-13

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How are the correct matches chosen What constraints, placed on the displacement vectors Three properties guide matching:

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