Model-based learning of segmentations

A method for integrating image segmentation information into geometric models is presented. The resulting object representation has advantages of both model-based and view-based representations, in that model geometry plus learned appearance information is used to improve the prediction of object appearance over purely geometric methods. The combined models are constructed over a training set of imagery using prior geometric models. Segmentation features are matched to the geometric models, and an evidential framework is used to characterize the segmentations of model features. To test the validity of the models, a pose adjustment system was modified to incorporate the prior segmentation information. Results indicate that the inclusion of the segmentation information significantly improves pose adjustment accuracy over using purely geometric information for model appearance