A Fuzzy Probabilistic Model for the Generalized Hough Transform

AbstructA fuzzy-probabilistic model of the Generalized Hough Transform (GHT) based on qualitative labeling of scene features is presented. Qualitative labeling of scene features is shown to be effective in pruning the search space of possible scene interpretations and also reducing the number of spurious interpretations explored by the GHT. Qualitative labeling of scene features is shown to result in the formulation of a weighted Generalized Hough Transform (WGHT) 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 are looked upon as membership function values for the fuzzy sets defined by these qualitative attributes. A fuzzy-probabilistic model for the WGHT is presented. Analytical expressions for the probability of accumulation of random votes are derived for the WGHT and compared with the corresponding expressions for the conventional GHT. The WGHT is shown to perform better than the conventional GHT. Experimental results on intensity and range images are presented.