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 by the 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 non-rigid models, allows nonlinear perspective transformations, and can optimally deal with occlusions and missing features. Experiments with rigid and non-rigid 3D hand gesture recognition support the theory. The likelihood based techniques show almost no decrease in classification performance when compared to performance with perfect correspondence knowledge. 1 INTRODUCTION The ability to deal with missing information is crucial in model-based vision systems. The feature correspondence pr..