Segmentation of overlapping objects
Lucas J. van Vliet, P.W. Verbeek · Data Archiving and Networked Services (DANS) · 1995
Traditional segmentation schemes are not capable of segmentation and subsequent labeling of overlapping objects.The reason is that overlap areas need multiple labels.As a first step one can reduce the overlap areas by representing objects by their edges.Using a priori knowledge about the shape of objects one can fit parametric models to the data.Examples are fitting procedures by least-squares models and the generalized Hough transform.Our method assumes no a priori knowledge of the objects to be detected.The only restriction we apply is a maximum curvature constraint to avoid discontinuities at corners.Using this constraint all lines are approximately straight at the scale of the selected window size.This is guaranteed for all highly oversampled images.The point-spread-function rounds all corners to a minimum contour radius and the oversampling factor scales this minimum contour radius according our requirements.In this abstract we focus our attention on contours.These contours can be either the original objects or the result of an edge detection scheme.