Agglomerative clustering on range data with a unified probabilistic merging function and termination criterion

Clustering methods, which are frequently employed for region-based segmentation, are inherently metric based. A fundamental problem with an estimation-based criterion is that as the amount of information in a region decreases, the parameter estimates become extremely unreliable and incorrect decisions are likely to be made. It is shown that clustering need not be metric based. A rigorous region merging probability function is used. It makes use of all information available in the probability densities of a statistical image model. By using this probability function as a termination criterion it is possible to produce segmentations in which all region merges are performed above some level of confidence