Kishore Bubna and Charles V. Stewart
Machine vision systems extract useful information from images in order to perform specific tasks. Estimating a geometric model of the world forms the basis of this extraction process. While some physical processes are well understood and easy to model mathematically, in most cases different models must be fit to the data and the best model is selected from these competing models. This process, generally referred as model selection, precedes parameter estimation when the model is not known a priori, and arises in diverse machine vision problems. For example, the best camera calibration model must be selected to get unbiased data, a deformation model must be selected to describe the deviations from CAD specifications when inspecting manufactured parts, and surfaces must be defined using the correct mathematical model in surface reconstruction for reverse engineering and 3D modeling. While the problem of parameter estimation has been well studied in computer vision, the associated problem of model selection has only received recent attention in the literature [6,17].