Probabilistic recognition networks: an application of influence diagrams to visual recognition
John Mark Agosta · 1992
This thesis builds a probabilistic model, called a which is an influence diagram for visual recognition, to determine the probability that an object appears in an image. The presence of each visual feature corresponds to one node in the network, thus there must be multiple paths connecting nodes in the network. Formulating one influence diagram for the entire recognition process demonstrates how evidence can be integrated consistently. At intermediate stages of the process, probabilities in the partially constructed network guide the process. This thesis demonstrates a method to determine the probabilities of existence of features from image evidence, and show how these probabilities propagate to determine which objects appear. We show how a network of the part-whole relations among objects and their features can be interpreted as a network of conditional probabilities. Further we show how such a network can be constructed dynamically from the evidence so that it scales nicely with the size of the problem, and can be solved by existing influence diagram solution techniques. Specifically we develop two kinds of network nodes, one to express vertical relations between objects and the set of features from which they are aggregated, and the other to express horizontal relations, such as ambiguity, among existence probabilities. The second kind of nodes have Conditional Inter-Causally Independent (CICI) distributions that express conflict among candidate hypotheses. As an example of an aggregation node, we develop a probability model for its counting aspect. Such a counting operation cannot be carried out with singly connected networks. CICI distributions are applied to model the degree of conflict among cylinder hypotheses due to their intersections. Constructing and evaluating an example network based on a image of a plumbing fixture demonstrates how the intersections of cylinder volumes tend to remove spurious cylinder hypotheses in the model.