Automatic selection of tuning parameters for feature extraction sequences

Computer vision algorithms are composed of different sub-algorithms often applied in sequence. Previous work on performance characterization illustrated how random perturbation models can be setup at various stages of an algorithm sequence for the input and output data. In this paper we address the issue of how one could utilize these random perturbation models in order to automate the selection of free parameters used in an algorithm sequence. We consider an operation sequence that involves edge finding, linking, corner finding and matching. Appropriate prior distributions for the parameters that describe the graytone/geometric characteristics of the image features are specified and validated by using an annotation process. The annotation process involves the manual specification (outlining) of the geometry and spatial extent of the image features. Statistics are gathered for parameters describing features of interest and non-interest (clutter features). The appropriate prior distributions are used to derive the theoretical expressions for feature detector performance over a given image population. These performance measures are then optimized to determine the tuning parameters for the feature detector(s)