  In this paper, a new framework for one-dimensional contour extraction from
discrete two-dimensional data sets is presented. Contour extraction is
important in many scientific fields such as digital image processing, computer
vision, pattern recognition, etc. This novel framework includes (but is not
limited to) algorithms for dilated contour extraction, contour displacement,
shape skeleton extraction, contour continuation, shape feature based contour
refinement and contour simplification. Many of the new techniques depend
strongly on the application of a Delaunay tessellation. In order to demonstrate
the versatility of this novel toolbox approach, the contour extraction
techniques presented here are applied to scientific problems in material
science, biology and heavy ion physics.
