Abstract
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, handwritten letter recognition, astronomy and heavy ion physics.
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