Relaxation and Its Role in Vision

It is argued that a visual system, especially one which handles imperfect data, needs a way of selecting the best consistent combination from among the many interrelated, locally plausible hypotheses about how parts or aspects of the visual input may be interpreted. A method is presented in which each hypothesis is given a supposition value between 0 and 1. A parallel relaxation I operator, based on the plausibilities of hypotheses and the logical relations between them, is then used to modify the supposition values, and the process is repeated until the best consistent set of hypotheses have supposition values of approximately 1, and the rest have values of approximately 0.

The method is incorporated in a program which can interpret configurations of overlapping rectangles as puppets. For this task it is possible to formulate all the potentially relevant hypotheses before using relaxation to select the best consistent set. For more complex tasks, it is necessary to use relaxation on the locally plausible interpretations to guide the search for locally less obvious ones. Ways of doing this are discussed.

Finally, an implemented system is presented which allows the user to specify schemas and inference rules, and uses relaxation to control the building of a network of instances of the schemas, when presented with data about some instances and relations between them.