A New Computational Framework For 2D Shape-Enclosing Contours

TL;DR

This paper introduces a versatile computational framework for extracting and refining 2D shape contours from discrete data, utilizing Delaunay tessellation, with applications across scientific disciplines.

Contribution

It presents a novel, comprehensive toolbox for 2D contour extraction and refinement, integrating multiple algorithms based on Delaunay tessellation, applicable to various scientific fields.

Findings

Effective contour extraction demonstrated in material science

Shape skeleton and feature extraction capabilities shown

Contour refinement improves accuracy and simplicity

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 and heavy ion physics.