Current implementations of relaxation labeling are homogeneous, where each pixel is in an identical relationship to a static neighbor set. These systems maintain the iterative probabilistic labeling but use a nonhomogeneous dynamic neighborhood to establish a local consistency. Neighborhoods are created at each iteration through the broadcasting and reception of label information according to semantically established broadcasting patterns for each label. Augmented relaxation labeling is a two stage process which contains a separate relaxation stage with a top-down direction capability for specific pixel label updating. Dynamic relaxation is a one step process where every pixel label is updated through the dynamic neighborhoods. Both labeling processes are demonstrated on simple line drawings.

The signature table method is a hierarchical approach for the recognition of binary patterns which are described by means of many features. The method was first applied by A. L. Samuel and his students for prediction of whether a given checker position was master quality or not. The variants of the methods discussed herein consider it as a general pattern recognition technique. Examples are presented to suggest both the potential and hazards of using the method for recognition of patterns. Advantages of the method are the extraction of nonlinear Boolean relationships among the variables, the use of incomplete data in a routine way, and that it sometimes may provide better prediction at less cost than multiple regression. The principle disadvantage, the lack of a theory to guide the choice of key parameters in the method, can sometimes be overcome by systematic computer search.

Intermittent faults are those faults whose effects on the behavior of a system are present only part of the time. A probabilistic model for intermittent faults in digital circuits is suggested. A procedure for the detection of such faults in combinational circuits is proposed. The procedure employs the repeated application of tests that test for these faults as if their effects were permanent. The procedure is analogous to a sequential statistical decision problem. Least upper bounds on the number of repetitions of tests that detect a particular fault are derived. These bounds are then employed in designing optimum detection experiments. Such an optimization problem is found to be equivalent to an integer programming problem.

A stochastic language is a set of words accepted by a probabilistic automaton with some cutpoint. The structure of the family of stochastic languages may not parallel the structure of the family of regular languages. Some stochastic languages are context-free languages which are nonregular. The basic questions of when the complement of a stochastic language is a stochastic language, or when the intersection or union of two stochastic languages is stochastic, have not been solved but have been illuminated by Turakainen's work. Turakainen proves that like the context-free languages, the intersection of a stochastic language and a regular language is a stochastic language. He shows as well that the union of a stochastic language and regular language is also a stochastic language. All right derivatives of a stochastic language are stochastic languages. In the same volume as Turakainen's paper, Nasu and Honda have proven what amounts to the fact that the reversal of a stochastic language is a stochastic language. They also show other basic properties.

The concept of "partition pair" is important for the decomposition of deterministic machines. The theory of stochastic machines would be enhanced with a suitable generalization. The definition of partition pairs presented by Bacon fails in that it does not reduce to the deterministic definition for a stochastic machine which is actually deterministic. A natural definition of partition pair is presented using probability distributions rather than states.