Regularity‐based Perceptual Grouping
This paper investigates perceptual grouping from a logical point of view, defining a grouping interpretation as a particular kind of logical expression, and then developing an explicit inference theory in terms of such expressions. First, a regularity‐based interpretation language is presented, in which an observed configuration is characterized in terms of the regularities(special configurational classes, e.g. nonaccidental properties) it obeys. The most preferred interpretation in such a system is shown to be the most‐regular (maximum “codimension”) model the observed configuration obeys, which is also the unique model in which it is generic(typical). Inference then reduces to a straiightforward exercise in LogicProgramming. Because generic model assignment involves negation, this reduction requires that a version of the Closed World Assumption (CWA) be adopted.