An attribute weighted distance transform
We present an attribute weighted distance transform (AWDT) in which the distance metric is differentially weighted by an attribute of an associated labeled foreground object. Both external and internal transforms are presented. Foreground objects in a binary image are labeled, their attributes computed and a weighting function derived from the attribute values. The weighting function is then integrated into Ragnemalms (1992) contour processing algorithm for computing the Euclidean distance transform. A threshold of the AWDT can be thought of as a dilation or erosion with a disk whose radius is spatially varying, according to attributes of nearby objects. We compare our method with that of Rosin and West (1995). Our method can be seen as an extension of this method. The usefulness of the method is illustrated in various examples.
euclidean distance transform mathematical morphology adaptive filtering