Qualitative features and the generalized hough transform

Abstract

In this paper we show how the use of qualitative features can enhance the performance of recognition and localization techniques, in particular, the Generalized Hough Transform. Qualitative features (i.e. scene features with qualitative attributes assigned to them) are shown to be effective in pruning the search space of possible scene interpretations and also reducing the number of spurious interpretations explored by the recognition and localization technique. The redundancy of the computed transform and the probability of spurious peaks of significant magnitude due to random accumulation of evidence are two criteria by which the performance of the Generalized Hough Transform is judged. The straightforward Generalized Hough Transform shows a high probability of spurious peaks of significant magnitude even for small values of redundancy and small magnitude of the search space of scene interpretations. The use of qualitative features enables us to come up with a weighted Generalized Hough Transform where each match of a scene feature with a model feature is assigned a weight based on the qualitative attributes assigned to the scene feature. These weights could be looked upon as membership function values for the fuzzy sets defined by these qualitative attributes. Analytic expressions for the probability of accumulation of random events within a bucket are derived for the weighted Generalized Hough Transform and compared with the corresponding expression for the straightforward Generalized Hough Transform. The weighted Generalized Hough Transform is shown to perform better than the straightforward Generalized Hough Transform. An experiment for the recognition of polyhedral objects from range images is described using dihedral junctions as features for matching and pose computation. The experimental results bring out the advantages of the weighted Generalized Hough Transform over the straightforward Generalized Hough Transform.