Abstract

Abstract. We present an image segmentation technique using the mor-phological Waterfall algorithm. Improvements in the segmentation are brought about by using improved gradients. These are based on the detection of object boundaries learnt from human segmentations intro-duced by Martin et al. (2004). We avoid the usual pitfall found when applying Watershed algorithms to these boundaries, namely that the boundary lines usually contain gaps, by making use of distance func-tions on the boundary image. Two types of distance function are used: the classic distance function and a distance function for numerical images recently introduced by Beucher (2005). Resulting segmentations are com-pared to human segmentations using the Berkeley segmentation bench-mark. The benchmark results show that the proposed segmentation al-gorithm produces segmentations comparable to those produced by the Normalised Cuts algorithm. 1