Delaunay triangulation

About: Delaunay triangulation is a research topic. Over the lifetime, 5816 publications have been published within this topic receiving 126615 citations. The topic is also known as: Delone triangulation.

A least-square radial point collocation method for adaptive analysis in linear elasticity

Abstract: This paper presents a least-square radial point collocation method (LS-RPCM) that is formulated based on the strong formulation and the local approximation using radial basis functions (RBFs). Aiming to solve the instability problem observed in the conventional RPCM using local nodes, a simple and yet effective procedure that uses the well-known least-square technique in a carefully designed manner has been proposed to restore the stability. Since stable solution can now be obtained, the LS-RPCM is then extended for adaptive analysis. Attractive features of the meshfree strong-form method that facilitate the implementation of adaptive analysis are demonstrated via a number of examples in this work. A robust residual based error estimator and a simple refinement procedure using Delaunay diagram are adopted in our adaptive scheme. Stable and accurate results are obtained in all the numerical examples.

Quality mesh generation for molecular skin surfaces using restricted union of balls

Abstract: Quality surface meshes for molecular models are desirable in the studies of protein shapes and functionalities. However, there is still no robust software that is capable to generate such meshes with good quality. In this paper, we present a Delaunay-based surface triangulation algorithm generating quality surface meshes for the molecular skin model. We expand the restricted union of balls along the surface and generate an /spl epsiv/-sampling of the skin surface incrementally. At the same time, a quality surface mesh is extracted from the Delaunay triangulation of the sample points. The algorithm supports robust and efficient implementation and guarantees the mesh quality and topology as well. Our results facilitate molecular visualization and have made a contribution towards generating quality volumetric tetrahedral meshes for the macromolecules.

Improved constructions of Delaunay based contour surfaces

Abstract: We revisit a method due to Boissonnat for surface reconstruc tion from parallel slices based on Delaunay triangulations. We e liminate the costly step of computing the three dimensional Dela unay triangulation, and instead compute the surface triangles d ir ctly. A non self-intersecting tiling is automatically guaranteed by these triangulations. Our experiment on some medical data shows that the method is effective. CR Categories: I.4.5 [Image Processing and Computer Vision]: Scene Analysis—Surface fitting;

Splitting a Delaunay Triangulation in Linear Time

Abstract: Computing the Delaunay triangulation of n points requires usually a minimum of Ω(n log n) operations, but in some special cases where some additional knowledge is provided, faster algorithms can be designed. Given two sets of points, we prove that, if the Delaunay triangulation of all the points is known, the Delaunay triangulation of each set can be computed in randomized expected linear time.

Image segmentation using topological persistence

Abstract: This paper presents a new hybrid split-and-merge image segmentation method based on computational geometry and topology using persistent homology. The algorithm uses edge-directed topology to initially split the image into a set of regions based on the Delaunay triangulations of the points in the edge map. Persistent homology is used to generate three types of regions: p-persistent regions, p-transient regions, and d-triangles. The p-persistent regions correspond to core objects in the image, while p-transient regions and d-triangles are smaller regions that may be combined in the merge phase, either with p-persistent regions to refine the core or with other p-transient and d-triangles regions to potentially form new core objects. Performing image segmentation based on topology and persistent homology guarantees several nice properties, and initial results demonstrate high quality image segmentation.