Modeling Positional Uncertainty in Object Recognition
Arthur R. Pope, David G. Lowe · 1994
Iterative alignment is one method for feature-based matching of an image and a model for the purpose of object recognition. The method alternately hypothesizes feature pairings and estimates a viewpoint transformation from those pairings; at each stage a refined transformation estimate is used to suggest additional pairings. This paper extends iterative alignment in the domain of 2D similarity transformations so that it represents the uncertainty in the position of each model and image feature, and that of the transformation estimate. A model describes probabilistically the significance, position, and intrinsic attributes of each feature, plus topological relations among features. A measure of the match between a model and an image integrates all four of these, and leads to an efficient matching procedure called probabilistic alignment. That procedure supports both recognition and a learning procedure for acquiring models from training images. By explicitly representing uncertainty, on...