Definition of a signal-to-noise ratio for object segmentation using polygonal MDL-based statistical snakes

Abstract

We address the problem of the characterization of segmentation performance of Minimum Description Length snake techniques in function of the noise which affects the image. It is shown that a parameter quantifying the contrast between the object of interest and the background can be defined from the Bhattacharyya distance. This contrast parameter is very general since it applies to several different noise statistics which belong to the exponential family. We illustrate its relevancy with a segmentation application using a polygonal snake descriptor. © Springer-Verlag Berlin Heidelberg 2003.