Robust feature selection for object recognition using uncertain 2D image data

The use of a small set of features is recurrent in the object recognition literature. If the image data is perfect with no sensor uncertainty and there are not incorrect feature correspondences between the model and the image, then the pose of the object can be computed with no error using these few correspondences. However, in most real cases the noise in the data will propagate into the pose. Moreover, the extent of the effect of the uncertainty will depend on the selection of the correspondences used to compute it. In this paper we address the problem of how to select these correspondences so that the effect of the data uncertainty on the pose estimation is minimized