Exploiting the common structure of some edge linking algorithms: an experimental study

Abstract

A class of edge linking algorithms with a common cost function was proposed by the authors. The cost function contains a quadratic regularization term and an image dependent term defined by a set of weighting functions. To define a new algorithm belonging to this class, the user has to specify a regularization matrix and a set of weighting functions which control the attraction of the model units towards the data. This paper compares the structure of the weighting functions associated with several well known algorithms which can be included in this framework (snakes, Kohonen (1982) maps, elastic nets, fuzzy c-means). This comparison allows a better understanding of their performance in edge linking operations. The paper also illustrates the design of new recursive schemes and describes techniques used to improve the convergence rate by more than one order of magnitude.