Feature Densities are Required for Computing Feature Correspondences

The feature correspondence problem is a classic hurdle in visual object-recognition concerned with determining the correct mapping between the features measured from the image and the features expected bythe model. In this paper we show that determining good correspondences requires information about the joint probability density over the image features. We propose "likelihood based correspondence matching" as a general principle for selecting optimal correspondences.The approach is applicable to nonrigid models, allows nonlinear perspective transformations, and can optimally dealwith occlusions and missing features.