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Journal ArticleDOI

Two algorithms for constructing a Delaunay triangulation

TLDR
This paper provides a unified discussion of the Delaunay triangulation and two algorithms are presented for constructing the triangulations over a planar set ofN points.
Abstract
This paper provides a unified discussion of the Delaunay triangulation. Its geometric properties are reviewed and several applications are discussed. Two algorithms are presented for constructing the triangulation over a planar set ofN points. The first algorithm uses a divide-and-conquer approach. It runs inO(N logN) time, which is asymptotically optimal. The second algorithm is iterative and requiresO(N 2) time in the worst case. However, its average case performance is comparable to that of the first algorithm.

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Citations
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Journal ArticleDOI

Methodology for Developing a Combined Bathymetric and Topographic Surface Model Using Interpolation and Geodata Reduction Techniques

TL;DR: In this article, an interpolation method based on the processing of datasets with different degrees of spatial data reduction is proposed for the development of a continuous elevation model in the coastal zones of inland waters.
Journal ArticleDOI

Comparing machine learning and interpolation methods for loop-level calculations

- 08 Jun 2022 - 
TL;DR: In this paper , the authors compare the performance of different machine learning methods on the Passarino-Veltman scalar and the two-loop self-energy master integral.

A countrywide tin elevations database

Antonio Ruiz
TL;DR: It is the aim to build a one piece TIN model with precision enough to generate cartographic quality contours at 1:5000 scale and smaller for the whole country and to deal with a triangulation in external memory.

Modeling and Computation of Vernacular Regions

TL;DR: A new geometric model is described and algorithms are developed to compute this model, which allows for more intelligent treatment of search queries like “hotels in the British Midlands”.
Book ChapterDOI

Applying Delaunay Triangulation Augmentation for Deep Learning Facial Expression Generation and Recognition

TL;DR: In this paper, a deconvolution neural network was used to encode the input images in a high-dimensional feature space and generate realistic expressions at varying intensities, which significantly improves the quality of images compared to previous comparable experiments and allows to create images with a higher resolution.
References
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Book

Computational geometry

Proceedings ArticleDOI

Closest-point problems

TL;DR: The purpose of this paper is to introduce a single geometric structure, called the Voronoi diagram, which can be constructed rapidly and contains all of the relevant proximity information in only linear space, and is used to obtain O(N log N) algorithms for most of the problems considered.