| dc.description.abstract | 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 | en |
