A New Computational Framework for 2D Shape-Enclosing Contour Extraction

Title: A New Computational Framework for 2D Shape-Enclosing Contour Extraction

Research Question: How can we develop a versatile and efficient framework for extracting contours from two-dimensional discrete data sets?

Methodology: The authors propose a new framework for contour extraction in two-dimensional discrete data sets. This framework includes algorithms for dilated contour extraction, contour displacement, shape skeleton extraction, contour continuation, shape feature-based contour refinement, and contour simplification. Many of these techniques rely on the application of a Delaunay tessellation. To demonstrate the versatility of this framework, the authors apply these techniques to various scientific problems in material science, biology, handwritten letter recognition, astronomy, and heavy ion physics.

Results: The authors present several examples of contour extraction from various data sets. These include material surfaces, bacterial colonies, handwritten letters, astronomical constellations, and freeze-out hyper-surfaces. The results show that the proposed framework is capable of handling a wide range of applications and data types.

Implications: The new contour extraction framework presented in this paper is highly versatile and efficient. It can be applied to a wide range of scientific problems and data types, making it a valuable tool for researchers in various fields. The framework's reliance on Delaunay tessellation ensures that it can handle both continuous and discrete data sets, further increasing its applicability.

Link to Article: https://arxiv.org/abs/0405029v2 Authors: arXiv ID: 0405029v2