Image Understanding Systems are complex and they are composed of different algorithms applied in sequence. A system for model-based recognition has three essential components: feature extraction, grouping and model matching. In each of these components, tuning parameters (thresholds) are often used. These parameters have been traditionally chosen by trial and error or from empirical data. In this paper we discuss a methodology for the analysis and design of IU algorithms and systems that follows sound systems engineering principles. We illustrate how the algorithm parameters can be optimally selected for a given image understanding algorithm sequence that accomplishes an IU task. The essential steps for each of the algorithm components involved are: component identification (performance characterization), and application domain characterization (achieved by an annotation). There is an optimization step that is used to optimize a criterion function relevant to the final task. Performance characterization of an algorithm involves the establishment of the correspondence between random perturbations in the input to the random perturbations in the output. This involves the setup of the model for the output random perturbations for a given ideal input model and input random perturbation model. Given these models and a criterion function, it is possible to characterize the performance of the algorithm as a function of its tuning parameters and automatically set the tuning parameters. The specification of the model for the population of ideal input data varies with problem domain. Domain-specific prior information on the parameters that describe the ideal input data is gathered during the annotation step. Appropriate theoretical approximations for the prior distributions are then specified, validated and utilized in computing the performance of the algorithm over the entire input population. Tuning parameters are selected to optimize the performance over the input population.
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