this article, please send e-mail to: tpami@computer.org, and reference IEEECS Log Number 107780

The performance of most vision systems (or subsystems) is significantly dependent on the choice of its various parameters or thresholds. The associated parameter search space is extremely large and nonsmooth; moreover, the optimal choices of the parameters are usually mutually dependent on each other. In this paper we offer a Bayesian network-based probabilistic formalism, which we call the parameter dependence networks (PDNs), to model, abstract, and analyze the parameter space behavior of vision systems. The various algorithm parameters are the nodes of the PDN and are associated with probabilistic beliefs about the optimality of their respective values. The links between the nodes capture the direct dependencies between them and are quantified by conditional belief functions. The PDN structure captures the interdependence among the parameters in a concise and explicit manner. We define information theoretic measures, based on these PDNs, to quantify the global parameter sensitivity and the strength of the interdependence of the parameters. These measures predict the general ease of parameter tuning and performance stability of the system. The PDNs can also be used to stochastically sample the parameter